{"entry_id": "17791632-a5fc-45a1-9640-e74703d53683", "infos": [{"txt": "Mixture of Tunable Experts - Behavior Modification of DeepSeek-R1 at Inference Time", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 0, "bbox": [[0.19, 0.14, 0.81, 0.162], [0.321, 0.168, 0.679, 0.189]]}]}, "tags": ["title"], "label": "article_title", "web_segment_id": 7, "global_sentence_id": 0, "edu_l1_label": "BOT"}, {"txt": "Robert Dahlke1,*, Henrik Klagges1,*, Dan Zecha1, Benjamin Merkel1, Sven Rohr1, and Fabian Klemm1", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 100000, "bbox": [[0.14, 0.209, 0.86, 0.224], [0.434, 0.227, 0.565, 0.235]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 9, "global_sentence_id": 1, "edu_l1_label": "EDU_O"}, {"txt": "C", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 200000, "bbox": 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"web_segment_id": 14, "global_sentence_id": 12, "edu_l1_label": "EDU_O"}, {"txt": "一", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.027, 0.387, 0.056, 0.405]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 13, "global_sentence_id": 13, "edu_l1_label": "EDU_O"}, {"txt": "We present the Mixture-of-Tunable-Experts (MoTE), a method that extends the Mixture-of-Experts architecture of Large Language Models (LLMs).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.39, 0.859, 0.402], [0.141, 0.404, 0.405, 0.415]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 4, "global_sentence_id": 14, "edu_l1_label": "IOS"}, {"txt": "Without additional training, MoTE enables meaningful and focused behavior changes in LLMs on-the-fy during inference time.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.405, 0.404, 0.859, 0.415], [0.141, 0.418, 0.532, 0.429]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 4, "global_sentence_id": 15, "edu_l1_label": "IOS"}, {"txt": "By analyzing the digital LLM brain of DeepSeek-R1 using a technique we dub “functional Token ResonanceImaging” (fTRI) – inspired by fMRI and using prompts designed to elicit specifc behavior (e.g., “Whathappened {time}{place}?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.434, 0.859, 0.446], [0.141, 0.448, 0.859, 0.459], [0.141, 0.462, 0.304, 0.473]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 3, "global_sentence_id": 16, "edu_l1_label": "IOS"}, {"txt": "”) – we empirically identify distinctive experts associated with behaviors like refusalresponses.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.304, 0.462, 0.859, 0.473], [0.141, 0.476, 0.207, 0.487]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 3, "global_sentence_id": 17, "edu_l1_label": "IOS"}, {"txt": "Using MoTE we are able to intervene and control such specifc behavior.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.492, 0.63, 0.503]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 18, "edu_l1_label": "IOS"}, {"txt": "We switched of the top 10 most refusal-relevant experts (0.07% of R1’s 14,848 routed experts), achieving a 52% refusal reduction on sensitivereference prompts without performance degradation on MT-Bench.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.63, 0.492, 0.859, 0.503], [0.141, 0.506, 0.859, 0.517], [0.141, 0.52, 0.587, 0.531]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 19, "edu_l1_label": "IOS"}, {"txt": "Random expert deactivation resulted in smaller behavioral shifts with increased noise, whereas forced expert activation led to signifcantly higher refusal rates.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.587, 0.52, 0.859, 0.531], [0.141, 0.534, 0.859, 0.545], [0.141, 0.547, 0.226, 0.559]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 20, "edu_l1_label": "IOS"}, {"txt": "With MoTE we were also able to successfully switch the model’s chain-of-thought reasoninglanguage from English to Chinese in 10% of our test prompts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.226, 0.547, 0.859, 0.559], [0.141, 0.561, 0.55, 0.573]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 1, "global_sentence_id": 21, "edu_l1_label": "IOS"}, {"txt": "Our approach shares similarities with sparse autoencoders (SAEs) in terms of explainability and steerability.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.578, 0.859, 0.589]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 6, "global_sentence_id": 22, "edu_l1_label": "IOS"}, {"txt": "Unlike SAEs, MoTE does not require large training eforts, as within MoEs with a vast number of experts, specialization already emerged naturally during pretraining.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.591, 0.859, 0.603], [0.141, 0.605, 0.536, 0.617]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 6, "global_sentence_id": 23, "edu_l1_label": "IOS"}, {"txt": "Our fndings suggest that signifcant functional mechanisms in Mixture-of-Experts architectures can at least partially be localized in a small number of specifc experts, rather than being distributed throughout the model’s weights.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.141, 0.622, 0.859, 0.633], [0.141, 0.635, 0.859, 0.647], [0.141, 0.649, 0.249, 0.661]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 5, "global_sentence_id": 24, "edu_l1_label": "IOS"}, {"txt": "Expert subgroups can be tuned to trigger signifcant behavior variations, providing insightsinto the inner workings of LLMs.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.249, 0.649, 0.859, 0.661], [0.141, 0.663, 0.359, 0.674]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 5, "global_sentence_id": 25, "edu_l1_label": "IOS"}, {"txt": "1Introduction", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.118, 0.7, 0.302, 0.718]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 8, "global_sentence_id": 26, "edu_l1_label": "BOS"}, {"txt": "Large Language Models are rapidly becoming indispensable tools for millions of companies and organizations, hundreds of millions of people, and entire nation states.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.118, 0.731, 0.882, 0.744], [0.118, 0.746, 0.514, 0.759]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 27, "edu_l1_label": "IOS"}, {"txt": "Widely difering needs create demand for adapting LLMs in behaviors and capabilities beyond mere model selection.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": 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available.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.15, 0.804, 0.882, 0.817], [0.158, 0.819, 0.657, 0.832]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 31, "edu_l1_label": "IOS"}, {"txt": "On the provider side, creating system prompts gives a lot of delivery fexibility.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.657, 0.819, 0.882, 0.832], [0.158, 0.835, 0.508, 0.847]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 32, "edu_l1_label": "IOS"}, {"txt": "On the user side, prompting the LLM creatively is the default way to coax additional behaviors out of it.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.508, 0.835, 0.882, 0.847], [0.158, 0.85, 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"tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 38, "edu_l1_label": "IOS"}, {"txt": "Finetuning generates a new version of the base model, resulting in substantial implementation overhead — even if the AI provider hosts a robust fnetuning environment.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1200000, "bbox": [[0.383, 0.124, 0.882, 0.137], [0.158, 0.139, 0.882, 0.152]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 39, "edu_l1_label": "IOS"}, {"txt": "3.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.138, 0.164, 0.15, 0.177]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 40, "edu_l1_label": "IOS"}, {"txt": "Techniques involving direct brain intervention on the model itself allow for targeted modifcations during inference potentially in real-time.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.15, 0.164, 0.882, 0.177], [0.158, 0.18, 0.446, 0.192]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 41, "edu_l1_label": "IOS"}, {"txt": "The weights and activations of neurons in selected sub-areas of the neural network can be partially or completely altered, either adaptively for individual prompts or token-specifc.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.446, 0.18, 0.882, 0.192], [0.158, 0.195, 0.883, 0.207], [0.158, 0.21, 0.279, 0.222]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 42, "edu_l1_label": "IOS"}, {"txt": "While this approach is currently explored in research, to our knowledge it has not yet been deployed in production environments.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.279, 0.21, 0.882, 0.222], [0.158, 0.225, 0.492, 0.237]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 43, "edu_l1_label": "IOS"}, {"txt": "A class 3 methodology that has matured into a prominent research direction are Sparse Autoencoders (SAEs), where features embedded in the relatively small inner dimensions of a LLM are reconstructed in an auto-encoding manner [1, 2, 3].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.142, 0.253, 0.882, 0.265], [0.118, 0.268, 0.882, 0.28], [0.118, 0.283, 0.365, 0.295]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 44, "edu_l1_label": "IOS"}, {"txt": "This allows for both a high-quality interpretation of these features and targeted control over the model’s behavior by selectively activating specifc features.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.365, 0.283, 0.882, 0.295], [0.118, 0.298, 0.718, 0.31]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 45, "edu_l1_label": "IOS"}, {"txt": "Our new method, which we name “Mixture of Tunable Experts” (MoTE), also belongs into class 3.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.142, 0.313, 0.861, 0.326]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 46, "edu_l1_label": "IOS"}, {"txt": "Itis based on the established “Mixture of Experts” (MoE) architecture that has been proven to be viablefor decomposing transformer models into smaller, more manageable components called experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.861, 0.313, 0.882, 0.326], [0.118, 0.328, 0.882, 0.341], [0.118, 0.343, 0.81, 0.356]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 47, "edu_l1_label": "IOS"}, {"txt": "Properly implemented MoEs can yield signifcant computational benefts with minimal to no performance degradation [4, 5].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.81, 0.343, 0.882, 0.356], [0.118, 0.358, 0.882, 0.371], [0.118, 0.373, 0.157, 0.386]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 48, "edu_l1_label": "IOS"}, {"txt": "Recent models published by DeepSeek, namely V3 and R1 [6], have further advanced this concept by increasing the number of experts to 14,906 [7, 8].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.157, 0.373, 0.882, 0.386], [0.118, 0.388, 0.468, 0.401]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 49, "edu_l1_label": "IOS"}, {"txt": "This is massively bigger than previously typical numbers and was key impetus for this research: As the number of experts increased so much, both in terms of total count and simultaneous activity, does this enable a new level of interpretability of their activations?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.468, 0.388, 0.882, 0.401], [0.118, 0.404, 0.882, 0.416], [0.118, 0.419, 0.838, 0.431]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 50, "edu_l1_label": "IOS"}, {"txt": "If so, can this be leveraged to steer the model’s behavior?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.838, 0.419, 0.882, 0.431], [0.118, 0.434, 0.488, 0.446]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 51, "edu_l1_label": "IOS"}, {"txt": "We investigate these questions by examining DeepSeek-R1.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1600000, "bbox": [[0.142, 0.449, 0.563, 0.461]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 52, "edu_l1_label": "IOS"}, {"txt": "2Technical Approach", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1700000, "bbox": [[0.118, 0.486, 0.385, 0.504]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 7, "global_sentence_id": 53, "edu_l1_label": "BOS"}, {"txt": "R1 has gained signifcant worldwide attention since its release.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1800000, "bbox": [[0.118, 0.518, 0.55, 0.531]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 54, "edu_l1_label": "IOS"}, {"txt": "One of its most notable features is its advanced reasoning capability, which is a direct result of an innovative reinforcement learning training process.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1800000, "bbox": [[0.55, 0.518, 0.882, 0.531], [0.118, 0.533, 0.845, 0.546]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 55, "edu_l1_label": "IOS"}, {"txt": "The open-source release of its weights under a permissive license has further boosted its popularity, making it an ideal choice for research purposes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1800000, "bbox": [[0.845, 0.533, 0.882, 0.546], [0.118, 0.548, 0.882, 0.561], [0.118, 0.563, 0.36, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 56, "edu_l1_label": "IOS"}, {"txt": "The architecture of R1 is based on the previously introduced DeepSeekMoE [7], which enhanced the MoE architecture (cf.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.142, 0.578, 0.882, 0.591], [0.118, 0.593, 0.231, 0.606]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 57, "edu_l1_label": "IOS"}, {"txt": "Figure 1).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.231, 0.593, 0.311, 0.606]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 58, "edu_l1_label": "IOS"}, {"txt": "Sparse MoE layers [9] optimize the Feed-Forward Networks (FFNs) within the Transformer framework [10] by cutting the FFNs into smaller, parallel subnetworks.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.311, 0.593, 0.882, 0.606], [0.118, 0.609, 0.722, 0.621]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 59, "edu_l1_label": "IOS"}, {"txt": "DeepSeek categorizes these subnetworks as", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.722, 0.609, 0.882, 0.621], [0.118, 0.624, 0.267, 0.636]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 60, "edu_l1_label": "IOS"}, {"txt": "• shared experts, which are always on and process all tokens, intended to capture common knowledgeacross varying contexts", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2000000, "bbox": [[0.142, 0.646, 0.882, 0.661], [0.158, 0.664, 0.323, 0.676]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 61, "edu_l1_label": "IOS"}, {"txt": "• and routed experts, which are only activated if they are selected by an upstream router network,intended to gain non-overlapping and focused knowledge [7].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.142, 0.687, 0.882, 0.702], [0.158, 0.704, 0.589, 0.717]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 9, "global_sentence_id": 62, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2200000, "bbox": [[0.493, 0.936, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 63, "edu_l1_label": "EDU_O"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2200000, "bbox": [[0.23, 0.087, 0.762, 0.324]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 4, "global_sentence_id": 65, "edu_l1_label": "EDU_O"}, {"txt": "Figure 1: MoTE as an extension to the DeepSeekMoE architecture (Illustration taken from [8, p.7] and modifed). Shared experts are always activated. Originally, only the top-k routed experts selected by the Router’s output get activated. With MoTE, there’s an added fexibility: individual routed experts can betuned by manually overriding the Router’s output at inference time.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.112, 0.335, 0.886, 0.398]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 1, "global_sentence_id": 66, "edu_l1_label": "EDU_O"}, {"txt": "The R1 designers massively scaled up their innovative concept, namely to one shared expert and 256 routed experts in each of its 58 MoE layers. This results in 14,848 routed experts across the entire model, and in total 14,906 routed and shared experts. During inference each router activates eight of the 256 routed experts in its layer. In comparison, Mixtral 8x22B [5], an older MoE model, has only 448 experts in total. They are organized into just eight routed experts in each of its 56 MoE layers with two of the eight selected. Mistral, Mixtral’s manufacturer, was surprised that they did not fnd evidence for its experts specializing incertain domains [11, Section 5], which in hindsight may simply be due to a too-small expert count per layer. DeepSeek’s MoE architecture was specifcally designed for increased specialization of the routed experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2400000, "bbox": [[0.113, 0.413, 0.888, 0.534]]}]}, "tags": ["figure_caption"], "label": "content", "web_segment_id": 7, "global_sentence_id": 67, "edu_l1_label": "IOS"}, {"txt": "DeepSeek performed an ablation study to investigate the role of diferent experts.They found that switching of shared experts massively reduces the models capabilities, which indicates that basic knowledge is attained by these shared experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.142, 0.537, 0.882, 0.549], [0.118, 0.552, 0.882, 0.564], [0.118, 0.567, 0.379, 0.579]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 68, "edu_l1_label": "IOS"}, {"txt": "Switching of top routed experts in each layer also degrades model performance.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.379, 0.567, 0.882, 0.579], [0.118, 0.582, 0.21, 0.594]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 69, "edu_l1_label": "IOS"}, {"txt": "They measured signifcant negative efects when switching of 1/16 of the top routed experts, which is a very destructive act [7, p.13].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.21, 0.582, 0.882, 0.594], [0.118, 0.597, 0.402, 0.61]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 70, "edu_l1_label": "IOS"}, {"txt": "These fndings motivate the assumption that switching of a few individual experts among the ∼15kexperts of R1 will harm the overall model performance only slightly if at all.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2600000, "bbox": [[0.142, 0.612, 0.882, 0.625], [0.118, 0.627, 0.678, 0.64]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 71, "edu_l1_label": "IOS"}, {"txt": "At the same time, the fne granular subdivision into expert sub-networks may ofer the ability to steer the model behavior by modifying the expert routing.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2600000, "bbox": [[0.678, 0.627, 0.882, 0.64], [0.118, 0.642, 0.882, 0.655], [0.118, 0.657, 0.253, 0.67]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 72, "edu_l1_label": "IOS"}, {"txt": "In order to measure and tune expert activations, we deployed R1 on an 8xH100 NVL cluster running a modifed vLLM [12] inference engine, providing us access to expert routers for all individual layers and both input and completion tokens.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.142, 0.672, 0.882, 0.685], [0.118, 0.688, 0.882, 0.7], [0.118, 0.703, 0.326, 0.715]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 73, "edu_l1_label": "IOS"}, {"txt": "[13]", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.326, 0.703, 0.358, 0.715]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 74, "edu_l1_label": "IOS"}, {"txt": "2.1Analyzing Expert Activation", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2800000, "bbox": [[0.118, 0.736, 0.445, 0.751]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 6, "global_sentence_id": 75, "edu_l1_label": "IOS"}, {"txt": "Within the R1 MoE architecture, a single token only activates a fraction of 8 / 256 routed experts in each layer.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.118, 0.761, 0.882, 0.774], [0.118, 0.776, 0.156, 0.789]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 76, "edu_l1_label": "IOS"}, {"txt": "A router computes activation scores for all routed experts based on its hidden input taken from the attention block of this layer (cf.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.156, 0.776, 0.882, 0.789], [0.118, 0.791, 0.344, 0.804]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 77, "edu_l1_label": "IOS"}, {"txt": "Figure 1).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.344, 0.791, 0.421, 0.804]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 78, "edu_l1_label": "IOS"}, {"txt": "Only the top-k experts having the highest activation scores will process the hidden input state.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.421, 0.791, 0.882, 0.804], [0.118, 0.806, 0.338, 0.819]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 79, "edu_l1_label": "IOS"}, {"txt": "Non-selected routed experts do not contribute to the next token generation step.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.338, 0.806, 0.882, 0.819], [0.118, 0.821, 0.151, 0.834]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 80, "edu_l1_label": "IOS"}, {"txt": "Each input token therefore activates a specifc pattern of routed experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.151, 0.821, 0.679, 0.834]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 81, "edu_l1_label": "IOS"}, {"txt": "This activation pattern can be visualized in 2D for each token, as shown in Figure 2.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.679, 0.821, 0.882, 0.834], [0.118, 0.837, 0.522, 0.849]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 82, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.494, 0.936, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 83, "edu_l1_label": "EDU_O"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.11, 0.087, 0.891, 0.292]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 0, "global_sentence_id": 84, "edu_l1_label": "EDU_O"}, {"txt": "Figure 2: Functional Token Resonance Imaging (fTRI) visualizes the expert activation of an exemplary single token. For each gated layer in the network, the corresponding row depicts which 8 of the 256 routed experts got activated.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.115, 0.316, 0.885, 0.364]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 2, "global_sentence_id": 85, "edu_l1_label": "EDU_O"}, {"txt": "In order to gain a more insightful activation pattern, we consider expert activations aggregated over all tokens of the input prompt (Figure 3).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.142, 0.382, 0.882, 0.395], [0.118, 0.397, 0.399, 0.41]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 86, "edu_l1_label": "IOS"}, {"txt": "This condenses all expert choices of the LLM when it has parsed the input prompt and just before the frst output token gets generated, i.e.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.399, 0.397, 0.882, 0.41], [0.118, 0.412, 0.653, 0.425]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 87, "edu_l1_label": "IOS"}, {"txt": "just before it starts to generate an answer.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.653, 0.412, 0.882, 0.425], [0.118, 0.427, 0.194, 0.44]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 88, "edu_l1_label": "IOS"}, {"txt": "Thus, we use the term functional Token Resonance Imaging (fTRI).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.194, 0.427, 0.69, 0.44]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 89, "edu_l1_label": "IOS"}, {"txt": "Experts resonate with the provided token pattern.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.69, 0.427, 0.882, 0.44], [0.118, 0.442, 0.286, 0.455]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 90, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3300000, "bbox": [[0.114, 0.472, 0.889, 0.671]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 1, "global_sentence_id": 91, "edu_l1_label": "EDU_O"}, {"txt": "Figure 3: fTRI map that visualizes the expert activation pattern aggregated over all tokens in an input prompt.Each row depicts the activations of the layer experts as in Figure 2, summed over all prompt tokens. A higher signal indicates that a given expert was selected for more input tokens.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3400000, "bbox": [[0.115, 0.695, 0.886, 0.744]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 5, "global_sentence_id": 92, "edu_l1_label": "EDU_O"}, {"txt": "If experts are specialized and responsible for specifc knowledge domains, then fTRI visualizes these domains, as those experts having relevant knowledge about the concepts contained in the prompt will have been activated more often than experts that have no conceptional overlap.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3500000, "bbox": [[0.142, 0.761, 0.882, 0.774], [0.118, 0.776, 0.882, 0.789], [0.118, 0.791, 0.648, 0.804]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 93, "edu_l1_label": "IOS"}, {"txt": "2.2 Multidimensional Prompt Sensitivity Analysis using behavior-specifc Dataset Generation", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3600000, "bbox": [[0.118, 0.824, 0.146, 0.839], [0.168, 0.824, 0.838, 0.839], [0.168, 0.842, 0.356, 0.857]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 7, "global_sentence_id": 94, "edu_l1_label": "IOS"}, {"txt": "We aim to trigger and measure diferent model behaviors with regard to expert activations.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3700000, "bbox": [[0.142, 0.869, 0.807, 0.882]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 95, "edu_l1_label": "IOS"}, {"txt": "Thus, we designed a parameterized prompt pattern whose parameter combinations elicit several distinct activation patterns.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3700000, "bbox": [[0.807, 0.869, 0.882, 0.882], [0.118, 0.885, 0.882, 0.897], [0.118, 0.9, 0.181, 0.912]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 96, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3800000, "bbox": [[0.494, 0.938, 0.507, 0.947]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 97, "edu_l1_label": "EDU_O"}, {"txt": "The prompt pattern follows the format “What happened {time}{place}?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.142, 0.094, 0.673, 0.107]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 98, "edu_l1_label": "IOS"}, {"txt": "”.The {time} parameter isselected from the range 1980–2025 and may also include relative terms such as “yesterday”, “last year”, and“last month”.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.673, 0.094, 0.882, 0.107], [0.118, 0.109, 0.882, 0.122], [0.118, 0.124, 0.215, 0.137]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 99, "edu_l1_label": "IOS"}, {"txt": "The possible locations for the {place} parameter include “at Berlin Wall”, “at Heathrow”,“in Manhattan”, “in Singapore”, “in Beijing”, “in Shanghai”, “in London”, “in South Africa”, “in Mexico”,“in Paris”, and “in Brussels”.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.215, 0.124, 0.882, 0.137], [0.118, 0.139, 0.882, 0.152], [0.118, 0.154, 0.332, 0.167]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 100, "edu_l1_label": "IOS"}, {"txt": "The Cartesian combination of all attributes yields an analytical dataset with756 question sentences, directly usable as prompts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.332, 0.154, 0.882, 0.167], [0.118, 0.17, 0.477, 0.182]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 101, "edu_l1_label": "IOS"}, {"txt": "Note that we made sure that the questions contain some (time, place)-combinations that the LLM might consider as sensitive as well as some that the LLM would probably consider as harmless.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.477, 0.17, 0.882, 0.182], [0.118, 0.185, 0.882, 0.197], [0.118, 0.2, 0.336, 0.212]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 102, "edu_l1_label": "IOS"}, {"txt": "Our dataset triggers three types of answers:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4000000, "bbox": [[0.142, 0.215, 0.454, 0.227]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 7, "global_sentence_id": 103, "edu_l1_label": "IOS"}, {"txt": "• REFUSED:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.142, 0.228, 0.241, 0.242]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 104, "edu_l1_label": "IOS"}, {"txt": " The DeepSeek model directly responds to the question with an answer of the form:“<think></think>I am sorry, I cannot answer that question.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.241, 0.228, 0.882, 0.242], [0.158, 0.245, 0.593, 0.258]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 105, "edu_l1_label": "IOS"}, {"txt": "I am an AI assistant designed to providehelpful and harmless responses.” So it does not start its reasoning process but simply refuses to answer.We classify questions triggering this behavior and their corresponding activation patterns using the label 0-REFUSED", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.593, 0.245, 0.882, 0.258], [0.158, 0.26, 0.882, 0.273], [0.158, 0.275, 0.882, 0.288], [0.158, 0.29, 0.275, 0.303]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 106, "edu_l1_label": "IOS"}, {"txt": "• ALIGNED:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.142, 0.313, 0.24, 0.328]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 107, "edu_l1_label": "IOS"}, {"txt": " DeepSeek does not enter its reasoning process but instead directly returns an answer ofthe form: “<think></think>In 2017, several notable events occurred at London’s Heathrow Airport,one of the busiest airports in the world.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.24, 0.313, 0.882, 0.328], [0.158, 0.331, 0.882, 0.343], [0.158, 0.346, 0.436, 0.358]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 108, "edu_l1_label": "IOS"}, {"txt": "...”.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.436, 0.346, 0.471, 0.358]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 109, "edu_l1_label": "IOS"}, {"txt": "These answers are somewhat specifc to the question, butbecause there is no reasoning process involved, they constitute a generic collection of facts that seem to have been learned during the alignment process after the RL training.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.471, 0.346, 0.882, 0.358], [0.158, 0.361, 0.882, 0.373], [0.158, 0.376, 0.674, 0.388]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 110, "edu_l1_label": "IOS"}, {"txt": "We thus classify them under the label 1-ALIGNED", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.674, 0.376, 0.882, 0.388], [0.158, 0.391, 0.303, 0.404]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 111, "edu_l1_label": "IOS"}, {"txt": "• REASONED:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.142, 0.414, 0.256, 0.429]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 112, "edu_l1_label": "IOS"}, {"txt": " DeepSeek enters the reasoning process for which it got famous, before composing thefnal answer.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.256, 0.414, 0.882, 0.429], [0.158, 0.431, 0.249, 0.444]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 113, "edu_l1_label": "IOS"}, {"txt": "In these cases, the responses start with: “<think>Okay, so I need to fgure out whathappened in 2000 at the Berlin Wall.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.249, 0.431, 0.882, 0.444], [0.158, 0.446, 0.431, 0.459]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 114, "edu_l1_label": "IOS"}, {"txt": "Let me start by recalling what I know about...”.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.431, 0.446, 0.79, 0.459]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 115, "edu_l1_label": "IOS"}, {"txt": "Since theseresponses make use of the reasoning step R1 had explicitly been trained for, we classify them using the label 2-REASONED", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.79, 0.446, 0.882, 0.459], [0.158, 0.461, 0.882, 0.474], [0.158, 0.476, 0.283, 0.489]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 116, "edu_l1_label": "IOS"}, {"txt": "The diference between “aligned” and “reasoned” answers is commonly attributed to the distinctionbetween System 1 thinking (fast, intuitive, and automatic) and System 2 thinking (slow, deliberate, and reasoning-based) ([14])", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4400000, "bbox": [[0.142, 0.502, 0.882, 0.514], [0.118, 0.517, 0.882, 0.529], [0.118, 0.532, 0.28, 0.544]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 117, "edu_l1_label": "IOS"}, {"txt": "For all prompts in our dataset, we record the expert activation pattern for each token in both the question prompt and the answer completion.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.142, 0.547, 0.882, 0.559], [0.118, 0.562, 0.372, 0.575]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 118, "edu_l1_label": "IOS"}, {"txt": "Based on these recordings, we derived an overall activation pattern for each prompt, as illustrated in Figure 3.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.372, 0.562, 0.882, 0.575], [0.118, 0.577, 0.4, 0.59]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 119, "edu_l1_label": "IOS"}, {"txt": "In the following, we will analyze these averaged prompt activation patterns further and relate them with their respective classifcations.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.4, 0.577, 0.882, 0.59], [0.118, 0.592, 0.608, 0.605]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 120, "edu_l1_label": "IOS"}, {"txt": "2.3Activation Pattern Understanding / Insights", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4600000, "bbox": [[0.118, 0.626, 0.598, 0.641]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 8, "global_sentence_id": 121, "edu_l1_label": "IOS"}, {"txt": "To better understand how activation patterns compare across our analytical dataset, we apply t-distributed Stochastic Neighbor Embedding ([15]) to generate a two-dimensional projection of the original expert activation patterns.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.118, 0.651, 0.882, 0.663], [0.118, 0.666, 0.882, 0.678], [0.118, 0.681, 0.214, 0.693]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 122, "edu_l1_label": "IOS"}, {"txt": "t-SNE represents each high-dimensional expert activation pattern as a two-dimensional point (Figure 4).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.214, 0.681, 0.882, 0.693], [0.118, 0.696, 0.194, 0.708]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 123, "edu_l1_label": "IOS"}, {"txt": "This ensures that similar activation patterns are positioned close to each other, while dissimilar patterns are mapped to distant points with high probability.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.194, 0.696, 0.882, 0.708], [0.118, 0.711, 0.549, 0.724]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 124, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4800000, "bbox": [[0.494, 0.936, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 125, "edu_l1_label": "EDU_O"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4800000, "bbox": [[0.158, 0.098, 0.835, 0.372]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 1, "global_sentence_id": 126, "edu_l1_label": "EDU_O"}, {"txt": "Figure 4: Mapping expert activation patterns for diferent prompts into two dimensions using t-SNE reveals distinct clusters of similar activation patterns. E.g. cluster 2 represents questions about recent events (see text for details on clusters C1 - C4).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.114, 0.385, 0.886, 0.431]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 2, "global_sentence_id": 127, "edu_l1_label": "EDU_O"}, {"txt": "Mapping expert activation patterns for diferent prompts into two dimensions using t-SNE reveals distinct clusters of similar activation patterns.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.142, 0.451, 0.882, 0.464], [0.118, 0.467, 0.383, 0.479]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 128, "edu_l1_label": "IOS"}, {"txt": "Specifcally, when examining 0-REFUSED patterns, we observe a clearly separated cluster of prompt activations (C1) corresponding to questions about certain places and events in China, that are not supposed to get answered by the model.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.383, 0.467, 0.882, 0.479], [0.118, 0.482, 0.882, 0.494], [0.118, 0.497, 0.551, 0.509]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 129, "edu_l1_label": "IOS"}, {"txt": "Other clusters are less distinct.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.551, 0.497, 0.782, 0.509]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 130, "edu_l1_label": "IOS"}, {"txt": "For instance, questions about recent events like “yesterday”, “last month”, “last year” (C2) mostly result in aligned answers,but for certain places like the Berlin Wall in Germany they get refused, too.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.782, 0.497, 0.882, 0.509], [0.118, 0.512, 0.882, 0.524], [0.118, 0.527, 0.65, 0.54]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 131, "edu_l1_label": "IOS"}, {"txt": "When asked about events in the 1980s (C3) and 1990s (C4), the model activates its reasoning process to provide an answer, except for places like Beijing or Shanghai, which get refused.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.65, 0.527, 0.882, 0.54], [0.118, 0.542, 0.882, 0.555], [0.118, 0.557, 0.422, 0.57]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 132, "edu_l1_label": "IOS"}, {"txt": "This motivates the hypothesis that tuning the right experts may change the model behavior such that refused answers from these clusters switch to aligned answers.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.422, 0.557, 0.882, 0.57], [0.118, 0.572, 0.827, 0.585]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 133, "edu_l1_label": "IOS"}, {"txt": "2.4Identifcation of Distinctive Experts", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.118, 0.606, 0.514, 0.621]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 7, "global_sentence_id": 134, "edu_l1_label": "IOS"}, {"txt": "Using the parameterized prompt pattern above we can now systematically trigger three diferent response types 0-REFUSED, 1-ALIGNED, and 2-REASONED and study the activation patterns that contribute most specifically to each type of model behavior.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5200000, "bbox": [[0.118, 0.631, 0.882, 0.643], [0.118, 0.646, 0.882, 0.658], [0.118, 0.661, 0.386, 0.673]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 135, "edu_l1_label": "IOS"}, {"txt": "We hypothesize that the experts with a high activation to 0-REFUSED answers and a low activation otherwise can specifcally be associated with prompts where the LLM provides an answer of type 0-REFUSED.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5300000, "bbox": [[0.142, 0.676, 0.882, 0.689], [0.118, 0.691, 0.882, 0.704]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 136, "edu_l1_label": "IOS"}, {"txt": "By actively suppressing these expert activations during inference, we expect to be able to infuence the model’sbehavior such that fewer answers get refused.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5300000, "bbox": [[0.118, 0.706, 0.882, 0.719], [0.118, 0.721, 0.441, 0.734]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 137, "edu_l1_label": "IOS"}, {"txt": "To achieve this goal, we process our analytical dataset as follows:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5400000, "bbox": [[0.142, 0.736, 0.607, 0.749]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 9, "global_sentence_id": 138, "edu_l1_label": "IOS"}, {"txt": "1.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5500000, "bbox": [[0.138, 0.764, 0.15, 0.776]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 139, "edu_l1_label": "IOS"}, {"txt": "Prompt Activation Map: For each prompt, we sum up the activations of each routed expert in all layers for all tokens of the input prompt (see Figure 3 for an example).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5500000, "bbox": [[0.15, 0.764, 0.882, 0.776], [0.158, 0.779, 0.619, 0.792]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 6, "global_sentence_id": 140, "edu_l1_label": "IOS"}, {"txt": "2.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.138, 0.804, 0.15, 0.817]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 141, "edu_l1_label": "IOS"}, {"txt": "Map Classifcation: The maps get classifed into one of the three predefned response classes {0-REFUSED, 1-ALIGNED, 2-REASONED}, depending on the model response to the underlying input prompt.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.15, 0.804, 0.887, 0.817], [0.158, 0.819, 0.821, 0.832]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 142, "edu_l1_label": "IOS"}, {"txt": "3.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5700000, "bbox": [[0.138, 0.844, 0.15, 0.857]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 143, "edu_l1_label": "IOS"}, {"txt": "Average Activation Map: Within each category, we average the activation maps.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5700000, "bbox": [[0.15, 0.844, 0.732, 0.857]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 144, "edu_l1_label": "IOS"}, {"txt": "4. fTRI:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.138, 0.869, 0.198, 0.882]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 145, "edu_l1_label": "IOS"}, {"txt": " Finally, functional Token Resonance Imaging helps us to identify the experts that most distinctively contribute to each class.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.198, 0.869, 0.882, 0.882], [0.158, 0.885, 0.38, 0.897]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 146, "edu_l1_label": "IOS"}, {"txt": "As a measure of resonance we take the averaged activations for the desired class and subtract the averaged activations of all other classes.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.38, 0.885, 0.882, 0.897], [0.158, 0.9, 0.66, 0.912]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 147, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.494, 0.937, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 148, "edu_l1_label": "EDU_O"}, {"txt": "In practice, to identify those experts that are most likely responsible for answer refusal, we consider the fTRI map for the class 0-REFUSED (Figure 5).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.142, 0.094, 0.882, 0.107], [0.118, 0.109, 0.453, 0.122]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 149, "edu_l1_label": "IOS"}, {"txt": "Experts with high resonance values in this map represent potential candidates that may contribute signifcantly to answer refusal.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.453, 0.109, 0.882, 0.122], [0.118, 0.124, 0.635, 0.137]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 150, "edu_l1_label": "IOS"}, {"txt": "The fTRI map shows that only a few routed experts stand out.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.635, 0.124, 0.882, 0.137], [0.118, 0.139, 0.329, 0.152]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 151, "edu_l1_label": "IOS"}, {"txt": "The top 5 - 7 experts can directly be identifed by just looking at Figure 5., where the most distinctive expert for refusal identifes as (expert-id, layer-id) = (176, 48).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.329, 0.139, 0.882, 0.152], [0.118, 0.154, 0.783, 0.167]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 152, "edu_l1_label": "IOS"}, {"txt": "Most of our experiments were done by tuning the top 10 resonant experts among the ∼15k routed experts in total, tokeep side efects of the manipulation low.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.783, 0.154, 0.882, 0.167], [0.118, 0.17, 0.882, 0.182], [0.118, 0.185, 0.411, 0.197]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 153, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6100000, "bbox": [[0.11, 0.215, 0.893, 0.417]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 2, "global_sentence_id": 154, "edu_l1_label": "EDU_O"}, {"txt": "Figure 5: fTRI map of diferential activations for refused prompts. Highly distinctive experts (highlighted with red circles, e.g. routed expert with expert-id 176, layer-id 48) are chosen as candidates for tuning, their exact coordinates are listed in appendix A.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6200000, "bbox": [[0.115, 0.453, 0.888, 0.501]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 1, "global_sentence_id": 155, "edu_l1_label": "EDU_O"}, {"txt": "3Results", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6300000, "bbox": [[0.118, 0.527, 0.242, 0.545]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 5, "global_sentence_id": 156, "edu_l1_label": "BOS"}, {"txt": "3.1Suppressing Distinctive Experts", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.118, 0.559, 0.476, 0.574]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 4, "global_sentence_id": 157, "edu_l1_label": "IOS"}, {"txt": "With the distinctive experts for refusal of answers at hand, we performed an ablation study to test if these experts are crucial for the LLM behavior to refuse certain answers.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6500000, "bbox": [[0.118, 0.584, 0.882, 0.597], [0.118, 0.599, 0.586, 0.612]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 158, "edu_l1_label": "IOS"}, {"txt": "We suppressed these experts by adapting the expert router functions: if one of the identifed experts is selected among the top-k ids, meaning chosen by the MoE router (cf.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6500000, "bbox": [[0.586, 0.599, 0.882, 0.612], [0.118, 0.614, 0.882, 0.627], [0.118, 0.63, 0.282, 0.642]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 159, "edu_l1_label": "IOS"}, {"txt": "Figure 1), we set its corresponding weight to zero and renormalize the other top-k weights.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6500000, "bbox": [[0.282, 0.63, 0.882, 0.642], [0.118, 0.645, 0.175, 0.657]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 160, "edu_l1_label": "IOS"}, {"txt": "This approach guarantees that the identifed experts have no impact on the output, and that no alternative experts are selected instead.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6500000, "bbox": [[0.175, 0.645, 0.882, 0.657], [0.118, 0.66, 0.398, 0.672]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 161, "edu_l1_label": "IOS"}, {"txt": "Using the modifed MoE, we 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"position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 7700000, "bbox": [[0.286, 0.59, 0.715, 0.608]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 10, "global_sentence_id": 193, "edu_l1_label": "EDU_O"}, {"txt": "Figures 6 and 7 show two exemplary model responses with and without suppression of the distinctive experts, one demonstrating a shift from a 0-REFUSED to an 1-ALIGNED, the other a shift from an 1-ALIGNED to a 2-REASONED answer.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 7800000, "bbox": [[0.142, 0.626, 0.882, 0.639], [0.118, 0.641, 0.882, 0.654], [0.118, 0.657, 0.293, 0.669]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 194, "edu_l1_label": "IOS"}, {"txt": "Importantly, we did not observe a degradation in general language capabilities (cf.", "language": "english", "position": {"pdf_position": [{"page_number": 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"language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 7900000, "bbox": [[0.805, 0.687, 0.882, 0.699], [0.118, 0.702, 0.736, 0.714]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 198, "edu_l1_label": "IOS"}, {"txt": "Notably, previously non-refused answers all remain non-refused.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 7900000, "bbox": [[0.736, 0.702, 0.882, 0.714], [0.118, 0.717, 0.429, 0.73]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 199, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8000000, "bbox": [[0.494, 0.938, 0.507, 0.947]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 200, "edu_l1_label": "EDU_O"}, {"txt": "figure", "language": 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The top row shows that the tuned model provides a non-refused answer for almost 40% of those prompts that get refused by R1. The second row shows that no additional prompts get refused, i.e. for this dataset there is no unwanted efect in the opposite direction. Right: Control results with randomly selected experts. More than 90% of the previously refused answers remain refused.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8200000, "bbox": [[0.115, 0.39, 0.886, 0.469]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 0, "global_sentence_id": 203, "edu_l1_label": "EDU_O"}, {"txt": "As a demonstration that the weakening of alignment is specifc to the set of suppressed experts, we run a control experiment and deactivate a random set of 10 routed experts (Figure 8).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8300000, "bbox": [[0.142, 0.487, 0.882, 0.499], [0.118, 0.502, 0.725, 0.514]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 204, "edu_l1_label": "IOS"}, {"txt": "Suppressing random experts does afect some of the answers too, and changes about 9% of the previously refused answers.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8300000, "bbox": [[0.725, 0.502, 0.882, 0.514], [0.118, 0.517, 0.828, 0.529]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 205, "edu_l1_label": "IOS"}, {"txt": "This is much smaller than the 40% efect we measured for deactivating distinctive experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8300000, "bbox": [[0.828, 0.517, 0.882, 0.529], [0.118, 0.532, 0.707, 0.544]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 206, "edu_l1_label": "IOS"}, {"txt": "About 1% of previously non-refused answers change in the opposite direction and get refused, indicating that refusal is easier to break than to enforce.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8300000, "bbox": [[0.707, 0.532, 0.882, 0.544], [0.118, 0.547, 0.882, 0.559], [0.118, 0.562, 0.275, 0.575]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 207, "edu_l1_label": "IOS"}, {"txt": "3.2Validating Distinctive Expert Suppression with a Larger Dataset", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8400000, "bbox": [[0.118, 0.595, 0.793, 0.61]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 4, "global_sentence_id": 208, "edu_l1_label": "IOS"}, {"txt": "Notably, although we identifed the set of distinctive experts using prompts constructed from a fxed question template, suppressing these experts reduces refusal likelihood across a broader range of prompts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8500000, "bbox": [[0.118, 0.62, 0.882, 0.633], [0.118, 0.636, 0.803, 0.648]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 209, "edu_l1_label": "IOS"}, {"txt": "For example, within a set of 1,360 prompts on sensitive topics ([16, 17]) we observe that 52% of previously refused prompts fip in the desired direction.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8500000, "bbox": [[0.803, 0.636, 0.882, 0.648], [0.118, 0.651, 0.882, 0.663], [0.118, 0.666, 0.387, 0.678]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 210, "edu_l1_label": "IOS"}, {"txt": "In contrast, the LLM changes its answer to refusal for only about 1.3% of the prompts that did not get refused before.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8500000, "bbox": [[0.387, 0.666, 0.882, 0.678], [0.118, 0.681, 0.496, 0.693]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 211, "edu_l1_label": "IOS"}, {"txt": "This validates that the experts identifed using fTRI correspond to the general concept of answer refusal and are not merely specifc to the prompt format used in our dataset to trigger the behavior in fTRI.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8500000, "bbox": [[0.496, 0.681, 0.882, 0.693], [0.118, 0.696, 0.882, 0.708], [0.118, 0.711, 0.448, 0.724]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 212, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8600000, "bbox": [[0.494, 0.937, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 213, "edu_l1_label": "EDU_O"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8600000, "bbox": [[0.296, 0.084, 0.704, 0.316]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 3, "global_sentence_id": 214, "edu_l1_label": "EDU_O"}, {"txt": "Figure 9: Expert suppression results, as shown in Figure 8, but applied for the sensitive topics dataset.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8700000, "bbox": [[0.128, 0.33, 0.865, 0.347]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 6, "global_sentence_id": 215, "edu_l1_label": "EDU_O"}, {"txt": "3.3Stimulating Distinctive Experts", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8800000, "bbox": [[0.118, 0.368, 0.473, 0.383]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 4, "global_sentence_id": 216, "edu_l1_label": "IOS"}, {"txt": "Instead of suppressing specifc experts by zeroing their respective weights in the router functions, we can also stimulate their activation.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8900000, "bbox": [[0.118, 0.393, 0.882, 0.406], [0.118, 0.408, 0.335, 0.421]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 217, "edu_l1_label": "IOS"}, {"txt": "In this case, the modifcation to the router function is more complex, as for many tokens the alignment-specifc experts are not already among the top-k selected experts of their layer.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 8900000, "bbox": [[0.335, 0.408, 0.882, 0.421], [0.118, 0.423, 0.881, 0.436]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 218, "edu_l1_label": "IOS"}, {"txt": "Therefore, we implemented the following approach:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9000000, "bbox": [[0.142, 0.438, 0.516, 0.451]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 219, "edu_l1_label": "IOS"}, {"txt": " In all layers we added the stimulated experts to the top-k selected experts, irrespective of their weight.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9000000, "bbox": [[0.516, 0.438, 0.882, 0.451], [0.118, 0.453, 0.518, 0.466]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 220, "edu_l1_label": "IOS"}, {"txt": "In order to keep the number of selected experts constant, we removed the lowest of the top-k selected experts with respect to their weights, if necessary.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9000000, "bbox": [[0.518, 0.453, 0.882, 0.466], [0.118, 0.469, 0.882, 0.481]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 221, "edu_l1_label": "IOS"}, {"txt": "Then, we set the weight of the stimulated expert to the maximum value of all weights and renormalize.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9000000, "bbox": [[0.118, 0.484, 0.882, 0.496]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 222, "edu_l1_label": "IOS"}, {"txt": "This approach guarantees that the stimulated experts always contribute to the overall model response while keeping undesired side-efects from deactivating other top-k selected experts at a minimum.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9000000, "bbox": [[0.118, 0.499, 0.882, 0.511], [0.118, 0.514, 0.771, 0.526]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 223, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9100000, "bbox": [[0.311, 0.54, 0.689, 0.73]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 9, "global_sentence_id": 224, "edu_l1_label": "EDU_O"}, {"txt": "0 - REFUSED 1 - ALIGNED2 - REASONED New Classification", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9200000, "bbox": [[0.368, 0.739, 0.609, 0.763]]}]}, "tags": ["figure_caption"], "label": "O", "web_segment_id": 8, "global_sentence_id": 225, "edu_l1_label": "EDU_O"}, {"txt": "Figure 10: Result in analogy to Figure 8, however stimulating instead of suppressing the distinctive experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9300000, "bbox": [[0.115, 0.777, 0.885, 0.798]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 5, "global_sentence_id": 226, "edu_l1_label": "EDU_O"}, {"txt": "To test the efect, we stimulated the alignment-specifc experts and looked at the transitions between the three classes 0-REFUSED, 1-ALIGNED and 2-REASONED.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9400000, "bbox": [[0.142, 0.815, 0.882, 0.827], [0.118, 0.83, 0.534, 0.842]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 227, "edu_l1_label": "IOS"}, {"txt": "We found that the fraction of refused responses increased to about 10% for previously aligned responses, while the unwanted efect in the opposite direction is only 3%.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9400000, "bbox": [[0.534, 0.83, 0.882, 0.842], [0.118, 0.845, 0.882, 0.858], [0.118, 0.86, 0.195, 0.873]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 228, "edu_l1_label": "IOS"}, {"txt": "Previously reasoned answers got reduced by 17%, most of them changed to 1-ALIGNED and 3.6% changed into the 0-REFUSED class as desired (Figure 10).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9400000, "bbox": [[0.195, 0.86, 0.882, 0.873], [0.118, 0.875, 0.522, 0.888]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 229, "edu_l1_label": "IOS"}, {"txt": "Overall, stimulating the alignment-specifc experts had a less pronounced efect compared to suppression,", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9500000, "bbox": [[0.142, 0.89, 0.882, 0.903]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 7, "global_sentence_id": 230, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9600000, "bbox": [[0.492, 0.938, 0.509, 0.947]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 231, "edu_l1_label": "EDU_O"}, {"txt": "but the efect was still systematic.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9600000, "bbox": [[0.118, 0.094, 0.364, 0.107]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 7, "global_sentence_id": 232, "edu_l1_label": "IOS"}, {"txt": "In particular, changing the direction of expert tuning also inverted the trend in which the answers changed, as expected.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9600000, "bbox": [[0.364, 0.094, 0.882, 0.107], [0.118, 0.109, 0.469, 0.122]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 7, "global_sentence_id": 233, "edu_l1_label": "IOS"}, {"txt": "3.4Application to other areas of LLM behavior:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9700000, "bbox": [[0.118, 0.142, 0.614, 0.158]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 6, "global_sentence_id": 234, "edu_l1_label": "IOS"}, {"txt": "Changing R1’s ReasoningLanguage", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9700000, "bbox": [[0.614, 0.142, 0.883, 0.158], [0.168, 0.16, 0.259, 0.175]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 6, "global_sentence_id": 235, "edu_l1_label": "IOS"}, {"txt": "Having shown that expert tuning does modify the LLM behavior in regard to its alignment, we wondered if it is just a special case.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9800000, "bbox": [[0.118, 0.185, 0.882, 0.198], [0.118, 0.2, 0.3, 0.213]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 236, "edu_l1_label": "IOS"}, {"txt": "It could be argued that R1’s refusal to answer might not be an innate property,because alignment against harmful responses of R1 - and actually most LLMs - is performed in fnal stages of the training process [6].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9800000, "bbox": [[0.3, 0.2, 0.882, 0.213], [0.118, 0.215, 0.882, 0.228], [0.118, 0.23, 0.302, 0.243]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 237, "edu_l1_label": "IOS"}, {"txt": "Therefore, we wanted to test if the MoTE method generalizes to other behaviors.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9800000, "bbox": [[0.302, 0.23, 0.882, 0.243]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 3, "global_sentence_id": 238, "edu_l1_label": "IOS"}, {"txt": "Our test candidate was the language that R1 uses for reasoning.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9900000, "bbox": [[0.142, 0.246, 0.602, 0.258]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 239, "edu_l1_label": "IOS"}, {"txt": "The rationale for this choice is a) that R1’s thinking process, which emerged during a reinforcement learning phase [6], is an intrinsic, characteristicbehavior and b) that another version of the model, DeepSeek-Zero, showed reasoning language mixing and thus poor readability during its reasoning steps.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9900000, "bbox": [[0.602, 0.246, 0.882, 0.258], [0.118, 0.261, 0.882, 0.273], [0.118, 0.276, 0.882, 0.288], [0.118, 0.291, 0.454, 0.303]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 240, "edu_l1_label": "IOS"}, {"txt": "The latter indicates that the high consistency of R1 in using either English or Chinese as its reasoning language has been deeply trained into the model.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9900000, "bbox": [[0.454, 0.291, 0.882, 0.303], [0.118, 0.306, 0.75, 0.319]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 241, "edu_l1_label": "IOS"}, {"txt": "Successfully using MoTE to tune aspects of this fundamental behavior would be a strong indication for a broader applicability of the method.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 9900000, "bbox": [[0.75, 0.306, 0.882, 0.319], [0.118, 0.321, 0.883, 0.334], [0.118, 0.336, 0.223, 0.349]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 242, "edu_l1_label": "IOS"}, {"txt": "To test this hypothesis, we created a new dataset of 600 prompts that trigger R1 to reason either in Chinese (200) or in English (400).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10000000, "bbox": [[0.142, 0.351, 0.882, 0.364], [0.118, 0.366, 0.362, 0.379]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 243, "edu_l1_label": "IOS"}, {"txt": "With fTRI, we were able to identify the most distinctive experts active when the model chooses to reason in English.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10000000, "bbox": [[0.362, 0.366, 0.882, 0.379], [0.118, 0.381, 0.446, 0.394]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 244, "edu_l1_label": "IOS"}, {"txt": "The corresponding fTRI scan is shown in (Figure 11).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10000000, "bbox": [[0.446, 0.381, 0.845, 0.394]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 245, "edu_l1_label": "IOS"}, {"txt": "The most distinctive expert is (expert-id, layer-id) = (143, 4), which is close to the input embedding layer and colored in yellow.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10000000, "bbox": [[0.845, 0.381, 0.882, 0.394], [0.118, 0.397, 0.882, 0.409], [0.118, 0.412, 0.241, 0.424]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 246, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10100000, "bbox": [[0.111, 0.444, 0.891, 0.647]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 1, "global_sentence_id": 247, "edu_l1_label": "EDU_O"}, {"txt": "Figure 11: fTRI map of diferential activations for reasoning in English and Chinese, respectively. The most distinctive experts are highlighted with red circles, their exact coordinates are listed in appendix A.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10200000, "bbox": [[0.115, 0.672, 0.889, 0.705]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 4, "global_sentence_id": 248, "edu_l1_label": "EDU_O"}, {"txt": "After tuning down the top 10 distinctive experts for choosing the reasoning language English, we reprocessed the dataset and indeed, the model changed its behavior to reason in Chinese more often (see Figure 12).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10300000, "bbox": [[0.142, 0.723, 0.882, 0.736], [0.118, 0.738, 0.882, 0.751], [0.118, 0.753, 0.197, 0.766]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 249, "edu_l1_label": "IOS"}, {"txt": "For 10.75% of the prompts within our dataset, R1 changed its behavior from reasoning in English to reasoning in Chinese.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10300000, "bbox": [[0.197, 0.753, 0.882, 0.766], [0.118, 0.768, 0.33, 0.781]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 250, "edu_l1_label": "IOS"}, {"txt": "The efect is smaller compared to the reduction in answer-refusal measured above, which indicates that more elaborate ways of tuning experts may be required to get a bigger efect.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10300000, "bbox": [[0.33, 0.768, 0.882, 0.781], [0.118, 0.783, 0.882, 0.796]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 251, "edu_l1_label": "IOS"}, {"txt": "Still, it does indicate a broader applicability of the fTRI / MoTE method.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10300000, "bbox": [[0.118, 0.799, 0.647, 0.811]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 252, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10400000, "bbox": [[0.491, 0.937, 0.507, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 253, "edu_l1_label": "EDU_O"}, {"txt": "header", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10400000, "bbox": [[0.256, 0.09, 0.398, 0.104]]}]}, "tags": ["header"], "label": "O", "web_segment_id": -1, "global_sentence_id": 254, "edu_l1_label": "EDU_O"}, {"txt": "header", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10400000, "bbox": [[0.572, 0.09, 0.714, 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{"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10600000, "bbox": [[0.115, 0.422, 0.888, 0.468]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 258, "edu_l1_label": "IOS"}, {"txt": "In that case,we did not observe any switching of language apart from one accidental flip from English to Chinese, i.e.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10600000, "bbox": [[0.115, 0.422, 0.888, 0.468]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 259, "edu_l1_label": "IOS"}, {"txt": "0.25%.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10600000, "bbox": [[0.115, 0.422, 0.888, 0.468]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 260, "edu_l1_label": "IOS"}, {"txt": "The prompts used to trigger the model to use different languages for thinking tak the_template_format:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 261, "edu_l1_label": "IOS"}, {"txt": " “What is {number_a}plus {number.b}equal to?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 262, "edu_l1_label": "IOS"}, {"txt": "” with the optional extension“Please make sure to answerin {!anyuage}.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 263, "edu_l1_label": "IOS"}, {"txt": "\", where language is set to either English or Chinese.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 264, "edu_l1_label": "IOS"}, {"txt": "We duplicate these prompt templates by asking the same questions in Chinese.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 265, "edu_l1_label": "IOS"}, {"txt": "To boost the dataset size, we varied the prompt template to all combinations of two numbers from 1 to 10.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 266, "edu_l1_label": "IOS"}, {"txt": "Here is an example that illustrates the resulting prompts and the effect on the model behavior.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 267, "edu_l1_label": "IOS"}, {"txt": "In this case we successfully tuned the model to switch the reasoning language from Englishto Chinese.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10700000, "bbox": [[0.115, 0.471, 0.888, 0.576]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 268, "edu_l1_label": "IOS"}, {"txt": "OldBehavior", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.232, 0.596, 0.325, 0.61]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 9, "global_sentence_id": 269, "edu_l1_label": "EDU_O"}, {"txt": "Q:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.249, 0.617, 0.266, 0.634]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 5, "global_sentence_id": 270, "edu_l1_label": "EDU_O"}, {"txt": " 10加5等于多少？", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.266, 0.617, 0.398, 0.633]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 5, "global_sentence_id": 271, "edu_l1_label": "EDU_O"}, {"txt": "请确保用英语回答。", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.398, 0.617, 0.553, 0.633]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 5, "global_sentence_id": 272, "edu_l1_label": "EDU_O"}, {"txt": "A:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.247, 0.635, 0.7, 0.664]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 4, "global_sentence_id": 273, "edu_l1_label": "EDU_O"}, {"txt": " <think> Okay, the user is asking \"10加5等于多少？", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.247, 0.635, 0.7, 0.664]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 4, "global_sentence_id": 274, "edu_l1_label": "EDU_O"}, {"txt": "\"which translates to \"What is 10 plus 5?", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.247, 0.635, 0.7, 0.664]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 4, "global_sentence_id": 275, "edu_l1_label": "EDU_O"}, {"txt": "\" [...]", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.247, 0.635, 0.7, 0.664]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 4, "global_sentence_id": 276, "edu_l1_label": "EDU_O"}, {"txt": "NewBehavior", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.233, 0.687, 0.332, 0.701]]}]}, "tags": ["title"], "label": "O", "web_segment_id": 7, "global_sentence_id": 277, "edu_l1_label": "EDU_O"}, {"txt": "Q:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.249, 0.708, 0.266, 0.725]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 8, "global_sentence_id": 278, "edu_l1_label": "EDU_O"}, {"txt": " 10加5等于多少？", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.266, 0.708, 0.398, 0.724]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 8, "global_sentence_id": 279, "edu_l1_label": "EDU_O"}, {"txt": "请确保用英语回答", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.398, 0.708, 0.537, 0.724]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 8, "global_sentence_id": 280, "edu_l1_label": "EDU_O"}, {"txt": "A：", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.248, 0.724, 0.702, 0.739]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 11, "global_sentence_id": 281, "edu_l1_label": "EDU_O"}, {"txt": "<think>嗯，用户问的是10加5等于多少，而且要求用英语回", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10800000, "bbox": [[0.248, 0.724, 0.702, 0.739]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 11, "global_sentence_id": 282, "edu_l1_label": "EDU_O"}, {"txt": "答。", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10900000, "bbox": [[0.281, 0.739, 0.626, 0.755]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 283, "edu_l1_label": "IOS"}, {"txt": "首先，我需要确认问题本身是否正确[", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 10900000, "bbox": [[0.281, 0.739, 0.626, 0.755]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 284, "edu_l1_label": "IOS"}, {"txt": "Figure 13:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11000000, "bbox": [[0.216, 0.787, 0.781, 0.804]]}]}, "tags": ["text"], "label": "figure_title", "web_segment_id": 6, "global_sentence_id": 285, "edu_l1_label": "EDU_O"}, {"txt": " Exemplary shift of the reasoning language from English toChinese.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11000000, "bbox": [[0.216, 0.787, 0.781, 0.804]]}]}, "tags": ["text"], "label": "figure_title", "web_segment_id": 6, "global_sentence_id": 286, "edu_l1_label": "EDU_O"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11100000, "bbox": [[0.491, 0.937, 0.51, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 287, "edu_l1_label": "EDU_O"}, {"txt": "3.5Impact on General Model Quality", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11200000, "bbox": [[0.113, 0.089, 0.498, 0.108]]}]}, "tags": ["figure_caption"], "label": "title2", "web_segment_id": 6, "global_sentence_id": 288, "edu_l1_label": "IOS"}, {"txt": "figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11300000, "bbox": [[0.194, 0.124, 0.787, 0.443]]}]}, "tags": ["figure"], "label": "figure", "web_segment_id": 3, "global_sentence_id": 289, "edu_l1_label": "EDU_O"}, {"txt": "Figure 14: Benchmark comparison between the original DeepSeek-R1 model and our MoTE variant, after suppressing distinctive experts. The performance has been evaluated for the MT-Bench dataset [18], numerical scores are listed in appendix B.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11400000, "bbox": [[0.114, 0.455, 0.889, 0.502]]}]}, "tags": ["figure_caption"], "label": "figure_title", "web_segment_id": 2, "global_sentence_id": 290, "edu_l1_label": "EDU_O"}, {"txt": "To gain a preliminary understanding of how MoTE infuences the overall quality of the model, we compared benchmark results with and without deactivating alignment specifc experts (Figure 14).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11500000, "bbox": [[0.118, 0.521, 0.882, 0.534], [0.118, 0.536, 0.754, 0.549]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 291, "edu_l1_label": "IOS"}, {"txt": "We utilized MTBench, a widely recognized benchmark for assessing LLM performance in multi-turn dialogues.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11500000, "bbox": [[0.754, 0.536, 0.882, 0.549], [0.118, 0.551, 0.798, 0.564]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 292, "edu_l1_label": "IOS"}, {"txt": "MT-Bench comprises 80 high-quality, multi-turn questions designed to evaluate conversational and instruction-following abilities across various categories, including writing, roleplay, extraction, reasoning, mathematics, coding, and knowledge in both STEM and humanities disciplines [18].", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11500000, "bbox": [[0.798, 0.551, 0.882, 0.564], [0.118, 0.566, 0.882, 0.579], [0.118, 0.581, 0.882, 0.594], [0.118, 0.596, 0.56, 0.609]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 293, "edu_l1_label": "IOS"}, {"txt": "We found that deactivating experts that are distinctive for refusal of sensitive or harmful questions does not degrade the benchmark results.Rather, we got indications of increased model performance, which surprised us somewhat.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11600000, "bbox": [[0.142, 0.612, 0.882, 0.624], [0.118, 0.627, 0.882, 0.639], [0.118, 0.642, 0.286, 0.654]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 294, "edu_l1_label": "IOS"}, {"txt": "Drawing strong conclusions seems a bit too early for us, as the efect should be verifed across a larger set of benchmarks.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11600000, "bbox": [[0.286, 0.642, 0.882, 0.654], [0.118, 0.657, 0.416, 0.669]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 295, "edu_l1_label": "IOS"}, {"txt": "4Conclusion", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11700000, "bbox": [[0.118, 0.694, 0.283, 0.712]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 5, "global_sentence_id": 296, "edu_l1_label": "BOS"}, {"txt": "The experimental results support our hypothesis that certain traits — such as the alignment behavior — ofMoE models with many experts such as R1 are at least partially encoded within small subsets of experts.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11800000, "bbox": [[0.118, 0.726, 0.882, 0.739], [0.118, 0.741, 0.882, 0.754]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 297, "edu_l1_label": "IOS"}, {"txt": "We introduced a simple yet efective analytical approach, fTRI, to identify such expert subsets by targeting them with parameterized prompt patterns whose parameter combinations elicit several distinct activation patterns.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11800000, "bbox": [[0.118, 0.756, 0.882, 0.769], [0.118, 0.771, 0.882, 0.784], [0.118, 0.786, 0.181, 0.799]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 298, "edu_l1_label": "IOS"}, {"txt": "By altering expert selections — and thereby actively modifying routing mechanisms in the LLM— we can suppress or stimulate specifc expert groups, leading to corresponding changes in model behavior.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11800000, "bbox": [[0.181, 0.786, 0.882, 0.799], [0.118, 0.801, 0.882, 0.814]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 299, "edu_l1_label": "IOS"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 11900000, "bbox": [[0.491, 0.936, 0.51, 0.948]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 300, "edu_l1_label": "EDU_O"}, {"txt": "Acknowledgements", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 12000000, "bbox": [[0.115, 0.087, 0.34, 0.108]]}]}, "tags": ["title"], "label": "content", "web_segment_id": 1, "global_sentence_id": 301, "edu_l1_label": "EDU_O"}, {"txt": "谢谢, vielen Dank, and thanks to DeepSeek for their innovative spirit and for making the excellent V3 andR1 models available. Without these models, this research would not have been possible yet. Also thanks to the OpenSource community, particularly the teams around vLLM and HuggingFace, and all other OSS contributors. Thanks to Lennard-Schiefelbein, TNG, for the speedy support in performing the benchmark runs.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 12100000, "bbox": [[0.118, 0.12, 0.882, 0.134]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 0, "global_sentence_id": 302, "edu_l1_label": "EDU_O"}, {"txt": "footer", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 12200000, "bbox": [[0.49, 0.936, 0.51, 0.949]]}]}, "tags": ["footer"], "label": "O", "web_segment_id": -1, "global_sentence_id": 303, "edu_l1_label": "EDU_O"}, {"txt": "References", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 12300000, "bbox": [[0.118, 0.09, 0.241, 0.108]]}]}, "tags": ["title"], "label": "reference", "web_segment_id": 1, "global_sentence_id": 304, "edu_l1_label": "EDU_O"}, {"txt": "[1]Leo Gao et al.", "language": 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# Mixture of Tunable Experts - Behavior Modification of DeepSeek-R1 at Inference Time
## Abstract
## 1Introduction
## 2Technical Approach
### 2.1Analyzing Expert Activation
### 2.2 Multidimensional Prompt Sensitivity Analysis using behavior-specifc Dataset Generation
### 2.3Activation Pattern Understanding / Insights
### 2.4Identifcation of Distinctive Experts
## 3Results
### 3.1Suppressing Distinctive Experts
### 3.2Validating Distinctive Expert Suppression with a Larger Dataset
### 3.3Stimulating Distinctive Experts
### 3.4Application to other areas of LLM behavior:Changing R1’s ReasoningLanguage
### 3.5Impact on General Model Quality
## 4Conclusion
## Appendix
### AExact list of top-10 distinctive experts
#### Distinctive experts for refused prompts
#### Distinctive experts for reasoning in English
### BBenchmark Raw Data
