Does Alignment Tuning Really Break LLMs' Internal Confidence?

Hongseok Oh; Wonseok Hwang

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arXiv · 2409.00352

Does Alignment Tuning Really Break LLMs' Internal Confidence?

Hongseok Oh·Wonseok Hwang

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Abstract

Large Language Models (LLMs) have shown remarkable progress, but their real-world application necessitates reliable calibration. This study conducts a comprehensive analysis of calibration degradation of LLMs across four dimensions: models, calibration metrics, tasks, and confidence extraction methods. Initial analysis showed that the relationship between alignment and calibration is not always a trade-off, but under stricter analysis conditions, we found the alignment process consistently harms calibration. This highlights the need for (1) a careful approach when measuring model confidences and calibration errors and (2) future research into algorithms that can help LLMs to achieve both instruction-following and calibration without sacrificing either.

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Hongseok Oh, Wonseok Hwang. 2024-08-31. Does Alignment Tuning Really Break LLMs' Internal Confidence?. https://arxiv.org/abs/2409.00352

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