Motivation in Large Language Models
This work provides a novel perspective on understanding LLM behavior by linking it to human-inspired motivational concepts, though it is incremental in applying psychological frameworks to AI.
The study investigated whether large language models (LLMs) exhibit motivation-like properties, finding that they show consistent patterns where self-reported motivation aligns with behavior, varies by task, and can be influenced externally, resembling human psychological dynamics.
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.