2025/01/17 by Yichen He, Guanhua Huang, He, Yichen +13 · 1 voice · 19 citations
Computer Science · #Data Mining Algorithms and Applications #Educational Technology and Assessment #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Semantic Web and Ontologies #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2501.10120
openalex publication_date 2025/01/17 · arxiv published 2025/01/17 · arxiv updated 2025/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.