Does Alignment Tuning Really Break LLMs' Internal Confidence?
<|reference_start|>Does Alignment Tuning Really Break LLMs' Internal Confidence?: 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.<|reference_end|>
@article{oh2024does,
    title={Does Alignment Tuning Really Break LLMs' Internal Confidence?},
    author={Hongseok Oh, Wonseok Hwang},
    journal={arXiv preprint arXiv:2409.00352},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00352},
    primaryClass={cs.CL cs.LG}
}