@inproceedings{chen-etal-2026-scirerankbench,
title = "{S}ci{R}erank{B}ench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with {LLM}s",
author = "Chen, Haotian and
Long, Qingqing and
Xiao, Meng and
Luo, Xiao and
Ju, Wei and
Zhou, Yuanchun and
Wang, Xuezhi and
Zhu, Hengshu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2613/",
pages = "59353--59380",
ISBN = "979-8-89176-459-0",
abstract = "Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, 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."
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<abstract>Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, 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.</abstract>
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%0 Conference Proceedings
%T SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
%A Chen, Haotian
%A Long, Qingqing
%A Xiao, Meng
%A Luo, Xiao
%A Ju, Wei
%A Zhou, Yuanchun
%A Wang, Xuezhi
%A Zhu, Hengshu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-scirerankbench
%X Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, 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.
%U https://aclanthology.org/2026.emnlp-main.2613/
%P 59353-59380
Markdown (Informal)
[SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs](https://aclanthology.org/2026.emnlp-main.2613/) (Chen et al., EMNLP 2026)
ACL
- Haotian Chen, Qingqing Long, Meng Xiao, Xiao Luo, Wei Ju, Yuanchun Zhou, Xuezhi Wang, and Hengshu Zhu. 2026. SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59353–59380, Budapest, Hungary. Association for Computational Linguistics.