# 指令微调如何影响模型置信度与词法多样性：一项针对三种模型的实证研究

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-13 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.virxact.com/items/cmsspyeyl0cm6roffse6mgdnw
- 原文链接：https://arxiv.org/abs/2608.13430

## AI 摘要

研究对比三组匹配的基础模型与指令微调模型在问答基准上的表现，发现指令微调持续改变答案置信度，但预测准确率变化有限，且基于似然的校准反而下降。指令微调对推理多样性影响不均：跨推理多样性一致下降，而表层词法多样性在不同模型和基准上方向和幅度各异。控制答案选择和推理长度后差异仍存在，表明置信度与推理多样性捕捉的是指令微调的不同效应。

## 正文

Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbalized overconfidence. In question answering, verbalized model overconfidence may be associated with the consistency of the generated supporting rationales. In this paper, we study whether corresponding changes in the lexical diversity of generated answer rationales accompany changes in model confidence induced by instruction tuning. We evaluate three matched base and instruction-tuned models across question-answering benchmarks and find that instruction tuning consistently alters answer confidence, despite limited changes in predictive accuracy and decreases in likelihood-based calibration. Secondly, we observe a non-uniform effect of instruction tuning on rationale diversity: cross-rationale diversity consistently decreases, whereas surface-level lexical diversity varies in both direction and magnitude across models and benchmarks. Finally, we find that these differences persist after controlling for answer selection and rationale length, confirming that confidence and rationale diversity capture distinct effects of instruction tuning.