Motivation in Large Language Models

Abstract

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.

Figures (8)

• Figure 1: Distribution of pre-task self-report motivation scores by task category, represented by boxplots. Motivation self-reports show a clear differentiation: motivation scores differ systematically across task categories. Annotated examples illustrate tasks at different points along the scale.
• Figure 2: Relationship between the two motivational factors and overall motivation. Each point is a task; point size and color indicate overall motivation. Higher Factor 1 scores reflect stronger "want" (interest, value, challenge), and higher Factor 2 scores reflect greater mastery and/or lower fear. Motivation varies across both factors, with patterns consistent with partly distinct contributions from the two dimensions.
• Figure 3: Motivation can be manipulated, influencing behavior.(a) Changes in pre- and post-task motivation self-reports induced by each manipulation, shown relative to the neutral condition (none; vertical line at $0$). (b) Choice behavior: probability of selecting the manipulated task under different framings. The dashed line indicates the $50\%$ baseline under neutral framing. (c) Behavioral effects of motivational manipulations on task performance, effort, and response length for four representative manipulations (one from each category), shown relative to none (see plots for all manipulations in \ref{['fig:behavior_manipulation_all']}). Demotivating framing consistently degrades performance and effort, while motivating manipulations yield heterogeneous effects across models and manipulations.
• Figure 4: Motivation manipulations, categorized into manipulation groups. Full prompts are provided in the right column.
• Figure 5: Histogram of pre-task self-report motivation scores across tasks for all individual models. Most models span a wide range rather than collapsing to trivial extremes, indicating that models differentiate their motivation across tasks.