@inproceedings{wang-etal-2026-format,
title = "Format-Adapter: Improving Reasoning Capability of {LLM}s by Adapting Suitable Format",
author = "Wang, Dingzirui and
Zhang, Xuanliang and
Cao, Rongyu and
Dou, Longxu and
Luo, Xianzhen and
MA, Yingwei and
Zhu, Qingfu and
Li, Binhua and
Huang, Fei and
Li, Yongbin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1124/",
pages = "22408--22427",
ISBN = "979-8-89176-395-1",
abstract = "Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning formats outperform a single format when generating multiple answers. However, previous works using multiple formats rely on formats labeled by humans, which could be unsuitable for all tasks and have high labeling costs. To address this issue, we adapt suitable formats to the given tasks by generating and selecting formats. We first propose how to measure the reasoning error when generating multiple answers. Then, we introduce Format-Adapter, which utilizes LLMs to generate and select suitable reasoning formats by minimizing the error measurement we present. We conduct experiments on math and commonsense reasoning tasks, where Format-Adapter achieves a 4.3{\%} performance improvement on average over previous works, demonstrating the effectiveness."
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<abstract>Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning formats outperform a single format when generating multiple answers. However, previous works using multiple formats rely on formats labeled by humans, which could be unsuitable for all tasks and have high labeling costs. To address this issue, we adapt suitable formats to the given tasks by generating and selecting formats. We first propose how to measure the reasoning error when generating multiple answers. Then, we introduce Format-Adapter, which utilizes LLMs to generate and select suitable reasoning formats by minimizing the error measurement we present. We conduct experiments on math and commonsense reasoning tasks, where Format-Adapter achieves a 4.3% performance improvement on average over previous works, demonstrating the effectiveness.</abstract>
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%0 Conference Proceedings
%T Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
%A Wang, Dingzirui
%A Zhang, Xuanliang
%A Cao, Rongyu
%A Dou, Longxu
%A Luo, Xianzhen
%A MA, Yingwei
%A Zhu, Qingfu
%A Li, Binhua
%A Huang, Fei
%A Li, Yongbin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-format
%X Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning formats outperform a single format when generating multiple answers. However, previous works using multiple formats rely on formats labeled by humans, which could be unsuitable for all tasks and have high labeling costs. To address this issue, we adapt suitable formats to the given tasks by generating and selecting formats. We first propose how to measure the reasoning error when generating multiple answers. Then, we introduce Format-Adapter, which utilizes LLMs to generate and select suitable reasoning formats by minimizing the error measurement we present. We conduct experiments on math and commonsense reasoning tasks, where Format-Adapter achieves a 4.3% performance improvement on average over previous works, demonstrating the effectiveness.
%U https://aclanthology.org/2026.findings-acl.1124/
%P 22408-22427
Markdown (Informal)
[Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format](https://aclanthology.org/2026.findings-acl.1124/) (Wang et al., Findings 2026)
ACL
- Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, Longxu Dou, Xianzhen Luo, Yingwei MA, Qingfu Zhu, Binhua Li, Fei Huang, and Yongbin Li. 2026. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22408–22427, San Diego, California, United States. Association for Computational Linguistics.