PaSa用大模型自动搜论文，找得准还快。
PaSa: An LLM Agent for Comprehensive Academic Paper Search
- 用强化学习训练智能体，自主调用工具、读论文、选参考文献。
- 在真实学术查询上，召回率比谷歌+GPT-4o高37.78%（top20）。
- 适合科研人员快速定位高质量文献，尤其复杂问题搜索。
我们提出PaSa，一个由大语言模型驱动的先进论文搜索代理。PaSa可自主决策，包括调用检索工具、阅读论文、筛选相关参考文献，以应对复杂学术查询并获取全面准确的结果。通过合成数据集AutoScholarQuery（含35,000条细粒度学术查询及对应顶会论文）优化模型，并构建真实查询基准RealScholarQuery评估其表现。尽管仅在合成数据上训练，PaSa仍显著优于现有基线，包括Google、Google Scholar、Google+GPT-4o、ChatGPT（支持搜索的GPT-4o）、GPT-o1以及由GPT-4o提示工程实现的PaSa-GPT-4o。其中，PaSa-7B在recall@20上超越最佳谷歌基线37.78%，recall@50上提升39.90%，且较PaSa-GPT-4o在召回率上高出30.36%，精度提升4.25%。模型、数据集与代码已开源：https://github.com/bytedance/pasa。
原文摘要 · Abstract (English)
We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholar queries. We optimize PaSa using reinforcement learning with a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained academic queries and corresponding papers sourced from top-tier AI conference publications. Additionally, we develop RealScholarQuery, a benchmark collecting real-world academic queries to assess PaSa performance in more realistic scenarios. Despite being trained on synthetic data, PaSa significantly outperforms existing baselines on RealScholarQuery, including Google, Google Scholar, Google with GPT-4o for paraphrased queries, ChatGPT (search-enabled GPT-4o), GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78% in recall@20 and 39.90% in recall@50, and exceeds PaSa-GPT-4o by 30.36% in recall and 4.25% in precision. Model, datasets, and code are available at https://github.com/bytedance/pasa.
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