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About this paper
Shijian Ma, Yunqi Huang, Yan Lin
DramaBench presents a six-dimensional framework to evaluate drama script continuation generated by large language models, employing an LLM Labeling + Statistical Analysis methodology. This benchmark reveals that even state-of-the-art models exhibit specialized strengths and weaknesses across creative dimensions, with no single model universally outperforming others.
Key insights
- No single state-of-the-art LLM exhibits universal excellence across all six dimensions, with models demonstrating specialized strengths (e.g., GPT-5.2 in logic, Qwen3-Max in emotion).
- The proposed "LLM Labeling + Statistical Analysis" methodology, which uses LLMs as structured annotators, yields objective and reproducible evaluations, showing moderate-to-substantial agreement with human experts for key dimensions.
- The six evaluation dimensions are empirically demonstrated to be largely independent, confirming that each captures a distinct aspect of drama script quality.
Summary by alphaXiv.
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