Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format

• URL: http://arxiv.org/abs/2506.23133v1
• Date: Sun, 29 Jun 2025 08:11:52 GMT
• Title: Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
• Authors: Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, Longxu Dou, Xianzhen Luo, Yingwei Ma, Qingfu Zhu, Wanxiang Che, Binhua Li, Fei Huang, Yongbin Li,
• Abstract summary: Prior works have shown that multiple reasoning formats outperform a single format when generating multiple answers.<n>We adapt suitable formats to the given tasks by generating and selecting formats.<n>We conduct experiments on math and commonsense reasoning tasks, where Format-Adapter achieves a 4.3% performance improvement on average over previous works.
• Score: 93.99600697438079
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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