发现大模型会无差别自信，提出新方法让其像人一样判断难易。
Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
- 让模型先估信心再答问题，打破答题与自信的绑定
- 在多个数据集上显著降低过度自信，提升对题目难易的敏感度
- 适合关注模型可信度与公平性的研究人员使用
心理学研究显示，人类在任务表现估计上存在偏差：简单任务下低估，复杂任务下高估。我们测试了Llama-3-70B-instruct、Claude-3-Sonnet和GPT-4o三款大模型在不同难度问答任务上的表现，发现模型虽不完全复制人类模式，但对任务难度敏感度较低；当以不同身份角色（如专家、普通人，或不同种族、性别、年龄）提示时，模型会输出带有刻板印象的自信评估，尽管其答案准确率不变。基于此，我们提出答案无关的自信估计（AFCE），通过两阶段提示：先仅请求信心评分，再单独请求答案。在MMLU和GPQA数据集上的实验表明，该方法显著缓解过自信问题，使模型对任务难度的响应更接近人类，提升校准度与可解释性。
原文摘要 · Abstract (English)
Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas -- e.g., expert vs layman, or different race, gender, and ages -- the models will respond with stereotypically biased confidence estimations even though their underlying answer accuracy remains the same. Based on these observations, we propose Answer-Free Confidence Estimation (AFCE) to improve confidence calibration and LLM interpretability in these settings. AFCE is a self-assessment method that employs two stages of prompting, first eliciting only confidence scores on questions, then asking separately for the answer. Experiments on the MMLU and GPQA datasets spanning subjects and difficulty show that this separation of tasks significantly reduces overconfidence and delivers more human-like sensitivity to task difficulty.
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