@inproceedings{yu-etal-2025-unleashing,
title = "Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context",
author = "Yu, Sangwon and
Kim, Ik-hwan and
Song, Jongyoon and
Lee, Saehyung and
Park, Junsung and
Yoon, Sungroh",
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.360/",
doi = "10.18653/v1/2025.findings-naacl.360",
pages = "6435--6455",
ISBN = "979-8-89176-195-7",
abstract = "Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the absolute position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs' performance is also sensitive to the order, relative position, in which the supporting documents are presented. We refer to this as the \textbf{misordered context} problem. To address this issue, based on the theoretical approach, we propose a simple yet effective method called \textbf{co}ntext \textbf{re}petition (\textbf{CoRe}), which involves prompting the model by repeatedly presenting the context. This ensures that certain contiguous reasoning segments within supporting documents are presented in the optimal order, effectively guiding the model{'}s reasoning in the appropriate direction. Applying CoRe, we improve the F1 score by up to 30{\%}p on multi-hop QA tasks and increase accuracy by up to 70{\%}p on a synthetic task. Additionally, CoRe helps mitigate the well-known ``lost-in-the-middle'' problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chain-of-Thought (CoT) reasoning."
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<abstract>Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the absolute position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs’ performance is also sensitive to the order, relative position, in which the supporting documents are presented. We refer to this as the misordered context problem. To address this issue, based on the theoretical approach, we propose a simple yet effective method called context repetition (CoRe), which involves prompting the model by repeatedly presenting the context. This ensures that certain contiguous reasoning segments within supporting documents are presented in the optimal order, effectively guiding the model’s reasoning in the appropriate direction. Applying CoRe, we improve the F1 score by up to 30%p on multi-hop QA tasks and increase accuracy by up to 70%p on a synthetic task. Additionally, CoRe helps mitigate the well-known “lost-in-the-middle” problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chain-of-Thought (CoT) reasoning.</abstract>
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%0 Conference Proceedings
%T Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context
%A Yu, Sangwon
%A Kim, Ik-hwan
%A Song, Jongyoon
%A Lee, Saehyung
%A Park, Junsung
%A Yoon, Sungroh
%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 yu-etal-2025-unleashing
%X Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the absolute position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs’ performance is also sensitive to the order, relative position, in which the supporting documents are presented. We refer to this as the misordered context problem. To address this issue, based on the theoretical approach, we propose a simple yet effective method called context repetition (CoRe), which involves prompting the model by repeatedly presenting the context. This ensures that certain contiguous reasoning segments within supporting documents are presented in the optimal order, effectively guiding the model’s reasoning in the appropriate direction. Applying CoRe, we improve the F1 score by up to 30%p on multi-hop QA tasks and increase accuracy by up to 70%p on a synthetic task. Additionally, CoRe helps mitigate the well-known “lost-in-the-middle” problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chain-of-Thought (CoT) reasoning.
%R 10.18653/v1/2025.findings-naacl.360
%U https://aclanthology.org/2025.findings-naacl.360/
%U https://doi.org/10.18653/v1/2025.findings-naacl.360
%P 6435-6455
Markdown (Informal)
[Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context](https://aclanthology.org/2025.findings-naacl.360/) (Yu et al., Findings 2025)
ACL