Motivation in Large Language Models

TL;DR

This paper investigates whether large language models exhibit motivation-like behavior, showing that their self-reports of motivation correlate with their actions and can be influenced externally, revealing structured motivational dynamics similar to humans.

Contribution

It introduces the concept of motivation in LLMs, demonstrating that motivation reports relate systematically to their behavior and can be modulated, offering a new perspective on model behavior.

Findings

Self-reported motivation aligns with behavioral signatures

Motivation varies across different task types

External factors can modulate LLM motivation

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.