LitSearch: A Retrieval Benchmark for Scientific Literature Search
This provides a new benchmark for retrieval systems to address the real-world challenge of scientific literature search, though it is incremental as it builds on existing retrieval evaluation methods.
The authors tackled the problem of evaluating retrieval systems for scientific literature search by introducing LitSearch, a benchmark with 597 realistic queries about ML/NLP papers, and found that state-of-the-art dense retrievers outperform BM25 by 24.8% in recall@5 and commercial search engines by up to 32 recall points.
Literature search questions, such as "Where can I find research on the evaluation of consistency in generated summaries?" pose significant challenges for modern search engines and retrieval systems. These questions often require a deep understanding of research concepts and the ability to reason across entire articles. In this work, we introduce LitSearch, a retrieval benchmark comprising 597 realistic literature search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions manually written by authors about their recently published papers. All LitSearch questions were manually examined or edited by experts to ensure high quality. We extensively benchmark state-of-the-art retrieval models and also evaluate two LLM-based reranking pipelines. We find a significant performance gap between BM25 and state-of-the-art dense retrievers, with a 24.8% absolute difference in recall@5. The LLM-based reranking strategies further improve the best-performing dense retriever by 4.4%. Additionally, commercial search engines and research tools like Google Search perform poorly on LitSearch, lagging behind the best dense retriever by up to 32 recall points. Taken together, these results show that LitSearch is an informative new testbed for retrieval systems while catering to a real-world use case.