Calibrating the Confidence of Large Language Models by Eliciting Fidelity

• URL: http://arxiv.org/abs/2404.02655v2
• Date: Wed, 09 Oct 2024 08:51:28 GMT
• Title: Calibrating the Confidence of Large Language Models by Eliciting Fidelity
• Authors: Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu,
• Abstract summary: Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. Post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. We propose a plug-and-play method to estimate the confidence of language models.
• Score: 52.47397325111864
• License:
• Abstract: Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper, we decompose the language model confidence into the \textit{Uncertainty} about the question and the \textit{Fidelity} to the answer generated by language models. Then, we propose a plug-and-play method to estimate the confidence of language models. Our method has shown good calibration performance by conducting experiments with 6 RLHF-LMs on four MCQA datasets. Moreover, we propose two novel metrics, IPR and CE, to evaluate the calibration of the model, and we have conducted a detailed discussion on \textit{Truly Well-Calibrated Confidence}. Our method could serve as a strong baseline, and we hope that this work will provide some insights into the model confidence calibration.

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