Alleviating Choice Supportive Bias in LLM with Reasoning Dependency Generation

• URL: http://arxiv.org/abs/2512.03082v1
• Date: Fri, 28 Nov 2025 08:52:05 GMT
• Title: Alleviating Choice Supportive Bias in LLM with Reasoning Dependency Generation
• Authors: Nan Zhuang, Wenshuo Wang, Lekai Qian, Yuxiao Wang, Boyu Cao, Qi Liu,
• Abstract summary: We present Reasoning Dependency Generation (RDG), a novel framework for generating unbiased reasoning data.<n>RDG automatically constructs balanced reasoning QA pairs, explicitly (un)modeling the dependencies between choices, evidences, and justifications.<n>Experiments show that LLMs fine-tuned on RDG-generated data demonstrate a 81.5% improvement in memory-based experiments and 94.3% improvement in the evaluation-based experiment.
• Score: 8.918979781532036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have demonstrated that some Large Language Models exhibit choice-supportive bias (CSB) when performing evaluations, systematically favoring their chosen options and potentially compromising the objectivity of AI-assisted decision making. While existing debiasing approaches primarily target demographic and social biases, methods for addressing cognitive biases in LLMs remain largely unexplored. In this work, we present the first solution to address CSB through Reasoning Dependency Generation (RDG), a novel framework for generating unbiased reasoning data to mitigate choice-supportive bias through fine-tuning. RDG automatically constructs balanced reasoning QA pairs, explicitly (un)modeling the dependencies between choices, evidences, and justifications. Our approach is able to generate a large-scale dataset of QA pairs across domains, incorporating Contextual Dependency Data and Dependency Decouple Data. Experiments show that LLMs fine-tuned on RDG-generated data demonstrate a 81.5% improvement in memory-based experiments and 94.3% improvement in the evaluation-based experiment, while maintaining similar performance on standard BBQ benchmarks. This work pioneers an approach for addressing cognitive biases in LLMs and contributes to the development of more reliable AI-assisted decision support systems.

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