SciRerankBench：面向科学文献 RAG-LLM 的重排序器基准

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Abstract:Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, \textit{two-stage} retrieval-augmented generated large language models (RAG-LLMs) have shown impressive advancements in this domain. Such a two-stage framework, especially the second stage (reranker), is particularly essential in the scientific domain, where subtle differences in terminology may have a greatly negative impact on the final factual-oriented or knowledge-intensive answers. Despite this significant progress, the potential and limitations of these works remain unexplored. In this work, we present a Scientific Rerank-oriented RAG Benchmark (SciRerankBench), for evaluating rerankers within RAG-LLMs systems, spanning five scientific subjects. To rigorously assess the reranker performance in terms of noise resilience, relevance disambiguation, and factual consistency, we develop three types of question-context-answer (Q-C-A) pairs, i.e., Noisy Contexts (NC), Semantically Similar but Logically Irrelevant Contexts (SSLI), and Counterfactual Contexts (CC). Through systematic evaluation of 13 widely used rerankers on five families of LLMs, we provide detailed insights into their relative strengths and limitations. To the best of our knowledge, SciRerankBench is the first benchmark specifically developed to evaluate rerankers within RAG-LLMs, which provides valuable observations and guidance for their future development.

Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2508.08742 [cs.CL]
	(or arXiv:2508.08742v3 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2508.08742 arXiv-issued DOI via DataCite

Submission history

From: Haotian Chen [view email]
[v1] Tue, 12 Aug 2025 08:36:23 UTC (766 KB)
[v2] Wed, 24 Sep 2025 07:37:00 UTC (766 KB)
[v3] Mon, 12 Oct 2026 06:38:20 UTC (19,308 KB)