English(EN) Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task

大型语言模型在自主学习任务中的目标选择与人类不同

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

arXiv上发表的一项新研究表明，大型语言模型（LLMs）在自主学习任务中选择目标时，并不能准确反映人类的偏好。研究人员发现，人类的行为涉及渐进式探索和多样化的目标追求，而模型则倾向于利用单一解决方案或表现出较低的性能，两者之间存在显著差异。即使采用了链式思考推理和角色扮演引导等技术，模型的改进也有限，这凸显了人类目标选择的独特性。 AI

影响强调了人类目标选择的独特性，并警示不要用当前的大型语言模型取代它。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Gaia Molinaro, Dave August, Danielle Perszyk, Anne G. E. Collins · 2026-10-19 04:00

语言模型目标选择与人类在自主学习任务中存在差异

arXiv:2603.03295v3 Announce Type: replace-cross Abstract: Whether in agentic workflows, social studies, or chat settings, large language models (LLMs) are increasingly being asked to replace humans in choosing which goals to pursue, rather than completing predefined tasks. Howeve…

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报道来源 [1]

语言模型目标选择与人类在自主学习任务中存在差异

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