Yes, you can use it this way to optimize the user experience
Cheney Zhang
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commented on
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16 days ago
How We Built a Semantic Highlight Model To Save Token Cost for RAG
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18 days ago
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: https://huggingface.co/zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
reacted
to
their
post
with 🔥
21 days ago
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: https://huggingface.co/zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
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commented on
How We Built a Semantic Highlight Model To Save Token Cost for RAG
16 days ago
reacted to
their
post with 🚀
18 days ago
Post
3152
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
reacted to
their
post with 🔥
21 days ago
Post
3152
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
posted
an
update
21 days ago
Post
3152
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
updated
a
model
22 days ago
upvoted
an
article
22 days ago
Article
How We Built a Semantic Highlight Model To Save Token Cost for RAG
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64
published
an
article
22 days ago
Article
How We Built a Semantic Highlight Model To Save Token Cost for RAG
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64
updated
6
datasets
about 1 month ago
zilliz/dureader-context-relevance-with-think
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76.7k
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35
zilliz/mmarco_chinese_200k-context-relevance-with-think
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216k
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57
zilliz/wikipedia_zh_cn_500k-context-relevance-with-think
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Updated
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500k
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31
zilliz/gooaq-context-relevance-130k-context-relevance-with-think
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Updated
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129k
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27
zilliz/natural_questions-context-relevance-with-think
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Updated
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13.4k
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28
zilliz/msmarco-context-relevance-with-think
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Updated
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262k
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73
updated
a
model
about 1 month ago
published
6
datasets
about 1 month ago
zilliz/msmarco-context-relevance-with-think
Viewer
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Updated
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262k
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73
zilliz/natural_questions-context-relevance-with-think
Viewer
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Updated
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13.4k
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28
zilliz/gooaq-context-relevance-130k-context-relevance-with-think
Viewer
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Updated
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129k
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27
zilliz/wikipedia_zh_cn_500k-context-relevance-with-think
Viewer
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Updated
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500k
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31
zilliz/mmarco_chinese_200k-context-relevance-with-think
Viewer
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Updated
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216k
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57
zilliz/dureader-context-relevance-with-think
Viewer
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Updated
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76.7k
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35