@inproceedings{joshi-etal-2025-coke,
    title = "{C}o{K}e: Customizable Fine-Grained Story Evaluation via Chain-of-Keyword Rationalization",
    author = "Joshi, Brihi  and
      Venkatapathy, Sriram  and
      Bansal, Mohit  and
      Peng, Nanyun  and
      Chang, Haw-Shiuan",
    editor = "Dhole, Kaustubh  and
      Clinciu, Miruna",
    booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
    month = jul,
    year = "2025",
    address = "Vienna, Austria and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.gem-1.31/",
    pages = "366--384",
    ISBN = "979-8-89176-261-9",
    abstract = "Evaluating creative text such as human-written stories using language models has always been a challenging task {--} owing to the subjectivity of multi-annotator ratings. To mimic the thinking process of humans, chain of thought (Wei et al., 2023) (CoT) generates free-text explanations that help guide a model{'}s predictions and Self-Consistency (Wang et al., 2022) (SC) marginalizes predictions over multiple generated explanations. In this study, we discover that the widely-used self-consistency reasoning methods cause suboptimal results due to an objective mismatch between generating `fluent-looking' explanations vs. actually leading to a good rating prediction for an aspect of a story. To overcome this challenge, we propose Chain-of-Keywords (CoKe), which generates a sequence of keywords before generating a free-text rationale, that guide the rating prediction of our evaluation language model. Then, we generate a diverse set of such keywords, and aggregate the scores corresponding to these generations. On the StoryER dataset, CoKe based on our small fine-tuned evaluation models not only reach human-level performance and significantly outperform GPT-4 with a 2x boost in correlation with human annotators, but also requires drastically less {\#} of parameters."
}