微调后大语言模型的不确定性沟通与元认知的泛化
Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models
AI总结：
研究通过监督微调提升大语言模型不确定性沟通能力，发现领域内校准和判别改善，但任务间迁移有限，多任务训练可促进泛化。
摘要：
背景。大语言模型越来越多地用于自信但错误的答案可能误导用户的环境。可靠的不确定性沟通需要一种元认知形式：监控自己的答案何时可能正确。然而，模型陈述的置信度通常与答案正确性对齐不佳。我们测试监督微调是否能改善不确定性沟通，以及增益是否能跨领域和任务格式迁移。方法。我们在常识、数学和开放式琐事问题上微调了两个模型。我们评估了单问题置信度估计（模型为一个答案报告数值置信度）和成对置信度比较（模型选择两个问题中哪一个更可能正确回答）。我们测试了来自训练领域的保留问题以及新的医学、法律和真实性基准。我们评估了微调前后的校准、判别和答案准确性。结果。我们表明微调改善了陈述置信度与观察准确性之间的对齐，并提高了模型将更高置信度分配给正确而非错误答案的能力。增益出现在训练领域内，并在较小程度上出现在新领域。然而，单任务训练不能可靠地在单问题置信度估计和成对置信度比较之间迁移。多任务微调在研究的模型和任务中产生了更广泛的增益。结论。大语言模型的不确定性沟通是可训练的，但跨元认知任务的迁移有限。对多个置信度任务的联合训练可能支持更广泛的泛化，尽管需要跨模型家族和元认知任务的进一步测试。
英文摘要：
Background. Large language models are increasingly used in settings where confident but incorrect answers can mislead users. Reliable uncertainty communication requires a form of metacognition: monitoring when one's own answers are likely to be correct. Yet models' stated confidence is often poorly aligned with answer correctness. We test whether supervised fine-tuning improves uncertainty communication and whether gains transfer across domains and task formats. Methods. We fine-tuned two models on general knowledge, mathematics, and open-ended trivia questions. We evaluated single-question confidence estimation, in which the model reports numeric confidence for one answer, and pairwise confidence comparison, in which it chooses which of two questions it is more likely to answer correctly. We tested held-out questions from training domains and new medical, legal, and truthfulness benchmarks. We assessed calibration, discrimination, and answer accuracy before and after fine-tuning. Results. Here we show that fine-tuning improves alignment between stated confidence and observed accuracy and increases the model's ability to assign higher confidence to correct than to incorrect answers. Gains occur within training domains and, to a lesser extent, in new domains. However, single-task training does not reliably transfer between single-question confidence estimation and pairwise confidence comparison. Multitask fine-tuning produces broader gains in the models and tasks studied here. Conclusions. Uncertainty communication in large language models is trainable, but transfer across metacognitive tasks is limited. Joint training on multiple confidence tasks may support broader generalization, although further tests across model families and metacognitive tasks are needed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
