CoCHI：从模型内部纠正 LLM 过度自信错误的新框架
Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
这篇论文把 LLM 过度自信的锅定位到具体的内部组件，再动手干预，校准指标全面改善还不掉准确率，思路挺新。
arXiv 上的一篇论文提出 CoCHI（Class-oriented Causal Head Intervention）框架，通过分析预测的正确率-置信度模式，定位并干预导致不同置信行为的模型内部组件。相比依赖事后调整或重新训练的现有校准方法，CoCHI 直接作用于模型内部机制，在降低过度自信错误的同时保留正确预测。实验覆盖多个 LLM，在 Expected Calibration Error、Negative Log-Likelihood 和 Brier Score 三项校准指标上均有提升，并在五个多选题问答基准上保持准确率。
Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
Modern large language models (LLMs) often exhibit overconfident errors, assigning high confidence to incorrect predictions and thereby undermining reliability in real-world use. Existing calibration methods typically rely on post-hoc adjustment or retraining, operating at the output level without addressing the underlying causes of miscalibration. We propose CoCHI, (Class-oriented Causal Head Intervention), a framework that improves calibration by identifying and intervening on internal components responsible for distinct confidence behaviours. By analysing predictions through correctness-confidence patterns, CoCHI enables targeted intervention on model internals, reducing overconfident errors while preserving correct predictions. Our approach provides a mechanistic perspective on calibration, linking reliability to identifiable structures within the model rather than treating it as an output-level issue. Experiments across multiple LLMs show consistent improvements in calibration metrics, including Expected Calibration Error, Negative Log-Likelihood, and Brier Score, while maintaining accuracy on five multiple-choice question answering benchmarks. These results suggest that effective calibration requires not only adjusting outputs, but also addressing the underlying causal mechanisms within the model.