Self-ensemble: Mitigating Confidence Distortion for Large Language Models

• URL: http://arxiv.org/abs/2506.01951v1
• Date: Mon, 02 Jun 2025 17:59:29 GMT
• Title: Self-ensemble: Mitigating Confidence Distortion for Large Language Models
• Authors: Zicheng Xu, Guanchu Wang, Guangyao Zheng, Yu-Neng Chuang, Alexander Szalay, Xia Hu, Vladimir Braverman,
• Abstract summary: Large Language Models exhibit a confidence distortion problem on multi-choice question-answering.<n>We propose Self-ensemble to solve this problem.<n> Experimental results on three LLMs and datasets demonstrate that Self-ensemble comprehensively addresses the confidence distortion problem.
• Score: 89.03110940871765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Large Language Models (LLMs) perform well in general fields, they exhibit a confidence distortion problem on multi-choice question-answering (MCQA), particularly as the number of answer choices increases. Specifically, on MCQA with many choices, LLMs suffer from under-confidence in correct predictions and over-confidence in incorrect ones, leading to a substantially degraded performance. To solve this problem, we propose Self-ensemble in this work. Our method splits the choices into several groups and ensembles LLM predictions across these groups to reach a final decision. The advantage of Self-ensemble is its plug-and-play nature, where it can be integrated into existing LLM architecture based on a designed attention mask and positional encoding, without requiring labeled datasets for parameter tuning. Experimental results on three LLMs and datasets demonstrate that Self-ensemble comprehensively addresses the confidence distortion problem of LLMs, outperforming standard inference as well as baseline methods.

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