@inproceedings{zhang-etal-2025-lexical,
title = "Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation",
author = "Zhang, Zhange and
Ma, Yuqing and
Wang, Yulong and
He, Shan and
Wang, Tianbo and
He, Siqi and
Wang, Jiakai and
Liu, Xianglong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1346/",
doi = "10.18653/v1/2025.acl-long.1346",
pages = "27758--27781",
ISBN = "979-8-89176-251-0",
abstract = "Retrieval-Augmented Generation (RAG) has proven effective in enhancing the factuality of LLMs' generation, making them a focal point of research. However, previous RAG approaches overlook the lexical diversity of queries, hindering their ability to achieve a granular relevance assessment between queries and retrieved documents, resulting in suboptimal performance. In this paper, we introduce a Lexical Diversity-aware RAG (DRAG) method to address the biases in relevant information retrieval and utilization induced by lexical diversity. Specifically, a Diversity-sensitive Relevance Analyzer is proposed to decouple and assess the relevance of different query components (words, phrases) based on their levels of lexical diversity, ensuring precise and comprehensive document retrieval. Moreover, a Risk-guided Sparse Calibration strategy is further introduced to calibrate the generated tokens that is heavily affected by irrelevant content. Through these modules, DRAG is capable of effectively retrieving relevant documents and leverages their pertinent knowledge to refine the original results and generate meaningful outcomes. Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6{\%} accuracy improvement on HotpotQA."
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<abstract>Retrieval-Augmented Generation (RAG) has proven effective in enhancing the factuality of LLMs’ generation, making them a focal point of research. However, previous RAG approaches overlook the lexical diversity of queries, hindering their ability to achieve a granular relevance assessment between queries and retrieved documents, resulting in suboptimal performance. In this paper, we introduce a Lexical Diversity-aware RAG (DRAG) method to address the biases in relevant information retrieval and utilization induced by lexical diversity. Specifically, a Diversity-sensitive Relevance Analyzer is proposed to decouple and assess the relevance of different query components (words, phrases) based on their levels of lexical diversity, ensuring precise and comprehensive document retrieval. Moreover, a Risk-guided Sparse Calibration strategy is further introduced to calibrate the generated tokens that is heavily affected by irrelevant content. Through these modules, DRAG is capable of effectively retrieving relevant documents and leverages their pertinent knowledge to refine the original results and generate meaningful outcomes. Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA.</abstract>
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%0 Conference Proceedings
%T Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation
%A Zhang, Zhange
%A Ma, Yuqing
%A Wang, Yulong
%A He, Shan
%A Wang, Tianbo
%A He, Siqi
%A Wang, Jiakai
%A Liu, Xianglong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-lexical
%X Retrieval-Augmented Generation (RAG) has proven effective in enhancing the factuality of LLMs’ generation, making them a focal point of research. However, previous RAG approaches overlook the lexical diversity of queries, hindering their ability to achieve a granular relevance assessment between queries and retrieved documents, resulting in suboptimal performance. In this paper, we introduce a Lexical Diversity-aware RAG (DRAG) method to address the biases in relevant information retrieval and utilization induced by lexical diversity. Specifically, a Diversity-sensitive Relevance Analyzer is proposed to decouple and assess the relevance of different query components (words, phrases) based on their levels of lexical diversity, ensuring precise and comprehensive document retrieval. Moreover, a Risk-guided Sparse Calibration strategy is further introduced to calibrate the generated tokens that is heavily affected by irrelevant content. Through these modules, DRAG is capable of effectively retrieving relevant documents and leverages their pertinent knowledge to refine the original results and generate meaningful outcomes. Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA.
%R 10.18653/v1/2025.acl-long.1346
%U https://aclanthology.org/2025.acl-long.1346/
%U https://doi.org/10.18653/v1/2025.acl-long.1346
%P 27758-27781
Markdown (Informal)
[Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation](https://aclanthology.org/2025.acl-long.1346/) (Zhang et al., ACL 2025)
ACL
- Zhange Zhang, Yuqing Ma, Yulong Wang, Shan He, Tianbo Wang, Siqi He, Jiakai Wang, and Xianglong Liu. 2025. Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27758–27781, Vienna, Austria. Association for Computational Linguistics.