src/about.py

from dataclasses import dataclass
from enum import Enum


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
    # General QA tasks
    nq = Task("nq", "exact_match", "NQ")
    triviaqa = Task("triviaqa", "exact_match", "TriviaQA")
    popqa = Task("popqa", "exact_match", "PopQA")
    # Multi-hop QA tasks
    hotpotqa = Task("hotpotqa", "exact_match", "HotpotQA")
    twowiki = Task("2wiki", "exact_match", "2wiki")
    musique = Task("musique", "exact_match", "Musique")
    bamboogle = Task("bamboogle", "exact_match", "Bamboogle")
    fictionalhot = Task("fictionalhot", "exact_match", "FictionalHot")


NUM_FEWSHOT = 0  # Change with your few shot
# ---------------------------------------------------


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">🔍 SearchAgent Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
# 🔍 SearchAgent Leaderboard

This leaderboard evaluates the performance of **search-augmented question answering systems** across various tasks, ranging from simple factual QA to complex multi-hop reasoning. Our evaluation addresses the inconsistency in experimental settings across prior works by providing a standardized comparison framework.

## 📊 Evaluation Tasks

We evaluate on a comprehensive set of benchmarks that test different aspects of search-augmented QA:

### General QA
- **NQ**: Natural Questions - QA based on real Google search queries from Wikipedia
- **TriviaQA**: large-scale dataset with questions from trivia websites and competitions, featuring complex entity relationships
- **PopQA**: A large-scale open-domain, entity-centric QA dataset (14k QA pairs), with questions generated by templating Wikidata knowledge tuples.

### Multi-Hop QA
- **HotpotQA**: the first large-scale dataset requiring reasoning across multiple Wikipedia paragraphs.
- **2Wiki**: A multi-hop QA dataset with explicit, annotated reasoning paths.
- **Musique**: A multi-hop QA benchmark of 2–4-hop questions constructed from five single-hop datasets.
- **Bamboogle**: A complex, cross-domain question set curated from queries that Google answers incorrectly to evaluate models’ compositional reasoning.

### Novel Evaluation: FictionalHot
- **FictionalHot**: A closed-world benchmark grounding questions in synthetic fictional entities to mitigate data contamination and enable reproducible evaluation. Questions are transformed from real-world scenarios to fictional ones while preserving reasoning structure.

## 🎯 Evaluation Metrics
Following standardized practices, we primarily use **Exact Match (EM)** as the main metric. A prediction is correct if its normalized string exactly matches any normalized reference answer (with lowercasing, punctuation removal, and whitespace normalization).

"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## 🔬 Evaluation Methodology

This leaderboard addresses the challenge of inconsistent experimental settings in search agent evaluation by providing standardized comparisons. Prior works vary significantly in:

1. **Corpora**: From static Wikipedia snapshots (2018, 2019) to live Internet access
2. **Test Sets**: Broad evaluation vs. focused multi-hop evaluation  
3. **Training Regimes**: No training to multi-dataset fine-tuning approaches
4. **Metrics**: Exact Match, F1, Substring matching, and LLM-as-a-judge evaluations

## 📋 Dataset Details & Challenges

### Data Contamination Problem
A critical issue in current benchmarks is **data contamination**, where high scores may reflect memorized pretraining knowledge rather than genuine procedural reasoning capabilities.

### Our Solution: FictionalHot
We introduce **FictionalHot**, a novel closed-world benchmark that:
- Grounds all questions in newly generated synthetic fictional entities
- Uses a three-step construction pipeline: sampling → GPT-based entity replacement → synthetic document generation
- Forces models to rely on procedural reasoning over provided documents
- Enables reproducible evaluation with a fixed knowledge source

### Benchmark Coverage
- **Corpus**: 2018 Wikipedia snapshot for reproducibility
- **Retrieval**: Top-k=3 with maximum T=4 tool-use turns per question

## 🔄 Experimental Setup

Following established practices, we:
- Fine-tune on unified NQ + HotpotQA training data
- Evaluate on Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct models  
- Use E5 embeddings for retrieval backend
- Apply standard Exact Match evaluation with string normalization


"""

EVALUATION_QUEUE_TEXT = """
## 📣 Model Submission via Community

We now accept submissions via the Space's Community (Discussions). This keeps the process simple and transparent.

- Go to the Community tab of this leaderboard Space:  
  https://huggingface.co/spaces/TencentBAC/SearchAgent_Leaderboard
- Create a new Discussion with title:  
  `Submission: <YourMethod>-<model_name>-<model_size>`
- Include the following in the post:
  - Model weights link (HF or GitHub)
  - Short method description
  - Evaluation JSON (inline or attached)

Example JSON:
```json
{
  "config": {
    "model_dtype": "torch.float16",
    "model_name": "YourMethod-Qwen2.5-7b-Instruct",
    "model_sha": "main"
  },
  "results": {
    "nq": {"exact_match": 0.45},
    "triviaqa": {"exact_match": 0.62},
    "popqa": {"exact_match": 0.38},
    "hotpotqa": {"exact_match": 0.41},
    "2wiki": {"exact_match": 0.33},
    "musique": {"exact_match": 0.15},
    "bamboogle": {"exact_match": 0.28},
    "fictionalhot": {"exact_match": 0.06}
  }
}
```

We will review your post and add your model to the leaderboard.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
% Key Search-Augmented QA Methods
@article{li2025reseek,
  title={ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards},
  author={Li, Shiyu and Tang, Yang and Wang, Yifan and Li, Peiming and Chen, Xi},
  journal={arXiv preprint arXiv:2510.00568},
  year={2025},
}

@article{luo2024search,
  title={Search-o1: Agentic Search-Enhanced Large Reasoning Models},
  author={Xiaoxi Li and Guanting Dong and Jiajie Jin and Yuyao Zhang and Yujia Zhou and Yutao Zhu and Peitian Zhang and Zhicheng Dou},
  journal={arXiv preprint arXiv:2501.05366},
  year={2025}
}

@article{songR1SearcherIncentivizingSearch2025,
  title={R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning},
  author={Song, Huatong and Jiang, Jinhao and Min, Yingqian and Chen, Jie and Chen, Zhipeng and Zhao, Wayne Xin and Fang, Lei and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2503.05592},
  year={2025}
}

@article{jin2025search,
  title={Search-r1: Training llms to reason and leverage search engines with reinforcement learning},
  author={Jin, Bowen and Zeng, Hansi and Yue, Zhenrui and Yoon, Jinsung and Arik, Sercan and Wang, Dong and Zamani, Hamed and Han, Jiawei},
  journal={arXiv preprint arXiv:2503.09516},
  year={2025}
}

@article{sunZeroSearchIncentivizeSearch2025,
  title={ZeroSearch: Incentivize the Search Capability of LLMs without Searching},
  author={Sun, Hao and Qiao, Zile and Guo, Jiayan and Fan, Xuanbo and Hou, Yingyan and Jiang, Yong and Xie, Pengjun and Zhang, Yan and Huang, Fei and Zhou, Jingren},
  journal={arXiv preprint arXiv:2505.04588},
  year={2025}
}

@article{zheng2025deepresearcher,
  title={Deepresearcher: Scaling deep research via reinforcement learning in real-world environments},
  author={Zheng, Yuxiang and Fu, Dayuan and Hu, Xiangkun and Cai, Xiaojie and Ye, Lyumanshan and Lu, Pengrui and Liu, Pengfei},
  journal={arXiv preprint arXiv:2504.03160},
  year={2025}
}

% Benchmark Datasets
@article{kwiatkowskiNaturalQuestionsBenchmark2019,
  title={Natural Questions: A Benchmark for Question Answering Research},
  author={Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others},
  journal={Transactions of the Association for Computational Linguistics},
  volume={7},
  pages={453--466},
  year={2019}
}

@article{yangHotpotQADatasetDiverse2018,
  title={HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering},
  author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William and Salakhutdinov, Ruslan and Manning, Christopher D.},
  booktitle={Proceedings of EMNLP},
  year={2018}
}

@article{trivediMuSiQueMultihopQuestions2022,
  title={MuSiQue: Multihop Questions via Single-hop Question Composition},
  author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
  journal={Transactions of the Association for Computational Linguistics},
  volume={10},
  pages={539--554},
  year={2022}
}
"""