贝叶斯教学使大语言模型中的概率推理成为可能
Linlu Qiu1, Fei Sha2, Kelsey Allen3,4,5
1Massachusetts Institute of Technology, Cambridge, MA, USA. linluqiu@mit.edu.
Nature communications
|January 7, 2026
概括
大型语言模型 (LLM) 可以学习贝叶斯推理技能. 教导LLM模仿贝叶斯预测显著提高了他们的信念更新和对新任务的概括能力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 越来越多地被部署为与用户和环境交互的代理.
- 有效的代理行为需要构建世界表征和概率学信仰.
- 个性化推需要LLM从交互历史中推断用户偏好.
研究的目的:
- 评估LLMs的信念更新能力与规范贝叶斯推理框架相比.
- 调查教学LLM模仿贝叶斯模型是否提高他们的推理和概括能力.
主要方法:
- 根据贝叶斯标准评估了LLM的信念更新性能.
- 经过训练,LLM可以模拟规范贝叶斯模型的预测.
- 评估了学习的信念更新技能对新任务的概括性.
主要成果:
- 在遵守贝叶斯的信念更新框架方面,LLM表现出显著的缺陷.
- 训练LLM模仿贝叶斯预测导致了对信念更新的大幅度改进.
- 增强的信念更新能力有效地对未见的任务进行了概括.
结论:
- 与贝叶斯代理相比,LLM在最佳信念更新方面表现出局限性.
- 通过模仿贝叶斯模型的学习,LLM可以获得和概括推理技能,特别是信念更新.
- 这项研究强调了LLM从数据中学习复杂推理能力的潜力.
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