mlsanityData to 14 Oct 2026

Graph-Based Alternatives to LLMs for Human Simulation

arXiv 2511.02135 ↗First posted 3 Nov 2025cs.CLcited by 1

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Abstract (arXiv)

Large language models (LLMs) have become a popular approach for simulating human behaviors, yet it remains unclear if LLMs are necessary for all simulation tasks. We study a broad family of close-ended simulation tasks, with applications from survey prediction to test-taking, and show that a graph neural network can match or surpass strong LLM-based methods. We introduce Graph-basEd Models for Human Simulation (GEMS) which formulates close-ended simulation as link prediction on a heterogeneous graph of individuals and choices. Across three datasets and three evaluation settings, GEMS matches or outperforms the strongest LLM-based methods while using three orders of magnitude fewer parameters. These results suggest that graph-based modeling can complement LLMs as an efficient and transparent approach to simulating human behaviors. Code is available at https://github.com/schang-lab/gems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 59 of its 87 references that are in the map. First posted on arXiv 3 Nov 2025.

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59 of 87 references.

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