M otive B ench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models? (2025)

Abstract: Large language models (LLMs) have been widely adopted as the core of agent frameworks in various scenarios, such as social simulations and AI companions. However, the extent to which they can replicate human-like motivations remains an underexplored question. Existing benchmarks are constrained by simplistic scenarios and the absence of character identities, resulting in an information asymmetry with real-world situations. To address this gap, we propose MotiveBench, which consists of 200 rich contextual scenarios and 600 reasoning tasks covering multiple levels of motivation. Using MotiveBench, we conduct extensive experiments on seven popular model families, comparing different scales and versions within each family. The results show that even the most advanced LLMs still fall short in achieving human-like motivational reasoning. Our analysis reveals key findings, including the difficulty LLMs face in reasoning about “love & belonging” motivations and their tendency toward excessive rationality and idealism. These insights highlight a promising direction for future research on the humanization of LLMs.

## introduction motivation is commonly conceptualized as an internal drive or psychological force that influences individuals to initiate and sustain goal-oriented activities @xcite . it serves as a key explanatory factor for understanding why people initiate, continue, or terminate specific behaviors at any given scenarios @xcite . motivation can be intrinsic, driven by internal values or preferences, or extrinsic, shaped by external rewards or punishments @xcite . ## motivebench construction we construct motivebench from scratch to avoid relying on existing scales or test items from psychology or sociology, thereby mitigating potential data leakage or contamination issues. figure 3 illustrates the ai-human collaboration framework. ## conclusions we introduce motivebench, the first systematic benchmark to evaluate the human-like motivational and behavioral reasoning ability of llms with detailed, realistic situations. our results reveal that even the most advanced llms still fall short in achieving human-like motivational reasoning. furthermore, most llms struggle with understanding "love & belonging" needs. in-depth analysis reveals specific differences in motivational reasoning between current llms and humans. by introducing motivebench, we aim to provide insights from a new dimension, enabling future models to exhibit more human-like cognition processes.