大型语言模型中的动机机制

摘要

动机作为人类行为的核心驱动力，深刻影响着决策制定、目标设定及任务表现。随着大语言模型与人类偏好的对齐程度日益提升，我们不禁追问：它们是否展现出类似动机的特质？本研究通过考察大语言模型是否会"呈现"不同层级的动机水平、这些动机表征如何关联其行为模式，以及外部因素能否对其产生影响，发现了一系列与人类心理学相呼应的结构化规律：模型自陈的动机水平不仅与不同的行为特征相匹配，还会随任务类型产生变化，且能被外部干预手段调节。这些发现表明，动机可作为解释大语言模型行为的连贯组织框架，系统性地联结模型的自我报告、选择偏好、努力程度与绩效表现，呈现出与人类心理学记载高度相似的动机动态机制。该研究视角不仅深化了我们对模型行为的理解，更揭示了其与人类心理概念的内在关联。

English

Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We examine whether LLMs "report" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them. Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations. These findings demonstrate that motivation is a coherent organizing construct for LLM behavior, systematically linking reports, choices, effort, and performance, and revealing motivational dynamics that resemble those documented in human psychology. This perspective deepens our understanding of model behavior and its connection to human-inspired concepts.

大型语言模型中的动机机制

Motivation in Large Language Models

摘要

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