发现大模型会像人一样报告动机水平并受外部影响。
Motivation in Large Language Models
- 让大模型自评动机，观察其行为变化。
- 动机报告与任务表现、努力程度相关联。
- 适合研究模型行为与人类心理的关联者。
动机是人类行为的核心驱动力，影响决策、目标设定和任务表现。随着大语言模型（LLMs）越来越符合人类偏好，我们探讨它们是否表现出类似动机的特征。通过实验，我们检验了大模型是否会报告不同动机水平，这些报告如何关联其行为，以及外部因素是否能影响它们。结果揭示出一致且有结构的模式，与人类心理学相呼应：自我报告的动机水平与不同行为特征相关，随任务类型变化，并可被外部干预调节。这些发现表明，动机是一个系统性组织大模型行为的框架，将报告、选择、努力与绩效紧密联系，揭示出类似于人类心理的动机动态。这一视角深化了我们对模型行为及其与人类概念关联的理解。
原文摘要 · Abstract (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.
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