找出影响大模型自信度的关键词语，通过抑制它们可校准信心而不降性能。
Identifying Influential N-grams in Confidence Calibration via Regression Analysis
- 用回归分析识别推理中影响信心的n-gram表达
- 多模型多数据集验证，发现模型推理时仍过度自信
- 抑制特定过自信表达可校准信心，适合模型优化者
尽管大语言模型通过显式推理提升性能，其回应常表现出过度自信，即使包含体现不确定性的语言表达。本文通过回归分析方法，将模型推理部分的语言表达作为自变量，预测其对应的信心值，探究特定n-gram与信心之间的关系。在多个模型和问答基准上，我们发现模型在推理过程中依然过度自信，并将其归因于某些特定语言信息。有趣的是，部分提取出的表达与测试阶段用于提升推理表现而有意插入的提示词一致。通过因果性检验和验证，我们确认这些语言信息确实影响信心水平，表明仅通过抑制这些过自信表达即可实现信心校准，且不会导致性能下降。
原文摘要 · Abstract (English)
While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions demonstrating uncertainty. In this work, we identify what linguistic expressions are related to confidence by applying the regression method. Specifically, we predict confidence of those linguistic expressions in the reasoning parts of LLMs as the dependent variables and analyze the relationship between a specific $n$-gram and confidence. Across multiple models and QA benchmarks, we show that LLMs remain overconfident when reasoning is involved and attribute this behavior to specific linguistic information. Interestingly, several of the extracted expressions coincide with cue phrases intentionally inserted on test-time scaling to improve reasoning performance. Through our test on causality and verification that the extracted linguistic information truly affects confidence, we reveal that confidence calibration is possible by simply suppressing those overconfident expressions without drops in performance.
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