Mind the Gap: The Divergence Between Human and LLM-Generated Tasks
Yi-Long Lu, Jiajun Song, Chunhui Zhang, Wei Wang
Abstract
Humans constantly generate a diverse range of tasks guided by internal motivations. While generative agents powered by large language models (LLMs) aim to simulate this complex behavior, it remains uncertain whether they operate on similar cognitive principles. To address this, we conducted a task-generation experiment comparing human responses with those of an LLM agent (GPT-4o). We find that human task generation is consistently influenced by psychological drivers, including personal values (e.g., Openness to Change) and cognitive style. Even when these psychological drivers are explicitly provided to the LLM, it fails to reflect the corresponding behavioral patterns. They produce tasks that are markedly less social, less physical, and thematically biased toward abstraction. Interestingly, while the LLM
BibTeX
@inproceedings{aaai2026_mindthegapthediv,
title = {Mind the Gap: The Divergence Between Human and LLM-Generated Tasks},
author = {Yi-Long Lu and Jiajun Song and Chunhui Zhang and Wei Wang},
booktitle = {AAAI 2026},
year = {2026}
}