@inproceedings{wang-etal-2025-fact,
title = "{FACT}: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval",
author = "Wang, Jinlin and
Wang, Suyuchen and
Xia, Ziwen and
Hong, Sirui and
Zhu, Yun and
Liu, Bang and
Wu, Chenglin",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.188/",
doi = "10.18653/v1/2025.findings-naacl.188",
pages = "3382--3392",
ISBN = "979-8-89176-195-7",
abstract = "Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts, especially during generation. This paper identifies a novel ``lost-in-the-middle'' phenomenon, where LLMs progressively lose track of critical information throughout the generation process, resulting in incomplete or inaccurate retrieval. To address this challenge, we introduce Find All Crucial Texts (FACT), an iterative retrieval method that refines context through successive rounds of rewriting. This approach enables models to capture essential facts incrementally, which are often overlooked in single-pass retrieval. Experiments demonstrate that FACT substantially enhances multi-fact retrieval performance across various tasks, though improvements are less notable in general-purpose QA scenarios. Our findings shed light on the limitations of LLMs in multi-fact retrieval and underscore the need for more resilient long-context retrieval strategies."
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<abstract>Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts, especially during generation. This paper identifies a novel “lost-in-the-middle” phenomenon, where LLMs progressively lose track of critical information throughout the generation process, resulting in incomplete or inaccurate retrieval. To address this challenge, we introduce Find All Crucial Texts (FACT), an iterative retrieval method that refines context through successive rounds of rewriting. This approach enables models to capture essential facts incrementally, which are often overlooked in single-pass retrieval. Experiments demonstrate that FACT substantially enhances multi-fact retrieval performance across various tasks, though improvements are less notable in general-purpose QA scenarios. Our findings shed light on the limitations of LLMs in multi-fact retrieval and underscore the need for more resilient long-context retrieval strategies.</abstract>
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%0 Conference Proceedings
%T FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval
%A Wang, Jinlin
%A Wang, Suyuchen
%A Xia, Ziwen
%A Hong, Sirui
%A Zhu, Yun
%A Liu, Bang
%A Wu, Chenglin
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wang-etal-2025-fact
%X Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts, especially during generation. This paper identifies a novel “lost-in-the-middle” phenomenon, where LLMs progressively lose track of critical information throughout the generation process, resulting in incomplete or inaccurate retrieval. To address this challenge, we introduce Find All Crucial Texts (FACT), an iterative retrieval method that refines context through successive rounds of rewriting. This approach enables models to capture essential facts incrementally, which are often overlooked in single-pass retrieval. Experiments demonstrate that FACT substantially enhances multi-fact retrieval performance across various tasks, though improvements are less notable in general-purpose QA scenarios. Our findings shed light on the limitations of LLMs in multi-fact retrieval and underscore the need for more resilient long-context retrieval strategies.
%R 10.18653/v1/2025.findings-naacl.188
%U https://aclanthology.org/2025.findings-naacl.188/
%U https://doi.org/10.18653/v1/2025.findings-naacl.188
%P 3382-3392
Markdown (Informal)
[FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval](https://aclanthology.org/2025.findings-naacl.188/) (Wang et al., Findings 2025)
ACL