# Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation Source: [Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:34:03.698Z Authors: Cyril Chhun, Fabian M. Suchanek, Chlo\\'e Clavel Published: 2024-05-22T15:56:52.000Z ## Abstract Storytelling is an integral part of human experience and plays a crucial role in social interactions. Thus, Automatic Story Evaluation (ASE) and Generation (ASG) could benefit society in multiple ways, but they are challenging tasks which require high-level human abilities such as creativity, reasoning and deep understanding. Meanwhile, Large Language Models (LLM) now achieve state-of-the-art performance on many NLP tasks. In this paper, we study whether LLMs can be used as substitutes for human annotators for ASE. We perform an extensive analysis of the correlations between LLM ratings, other automatic measures, and human annotations, and we explore the influence of prompting on the results and the explainability of LLM behaviour. Most notably, we find that LLMs outperform current automatic measures for system-level evaluation but still struggle at providing satisfactory explanations for their answers. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Story Evaluation on HANNA (test)](): Story Evaluation; HANNA (test); Kendall Correlation: 0.25. ## Original sources and implementation [Original paper]() [PDF]()