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arXiv · 2510.18932

Evaluating LLM Story Generation through Large-scale Network Analysis of Social Structures

Hiroshi Nonaka·K. E. Perry
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Abstract

Evaluating the creative capabilities of large language models (LLMs) in complex tasks often requires human assessments that are difficult to scale. We introduce a novel, scalable methodology for evaluating LLM story generation by analyzing underlying social structures in narratives as signed character networks. To demonstrate its effectiveness, we conduct a large-scale comparative analysis using networks from over 1,200 stories, generated by four leading LLMs (GPT-4o, GPT-4o mini, Gemini 1.5 Pro, and Gemini 1.5 Flash) and a human-written corpus. Our findings, based on network properties like density, clustering, and signed edge weights, show that LLM-generated stories consistently exhibit a strong bias toward tightly-knit, positive relationships, which aligns with findings from prior research using human assessment. Our proposed approach provides a valuable tool for evaluating limitations and tendencies in the creative storytelling of current and future LLMs.

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BibTeXRIS

Hiroshi Nonaka, K. E. Perry. 2025-10-21. Evaluating LLM Story Generation through Large-scale Network Analysis of Social Structures. https://arxiv.org/abs/2510.18932

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