CoKe: Customizable Fine-Grained Story Evaluation via Chain-of-Keyword Rationalization
This work addresses the challenge of subjective multi-annotator ratings in story evaluation for NLP researchers, offering a novel method to improve evaluation accuracy.
The paper tackled the problem of evaluating creative text like human-written stories by addressing suboptimal results from self-consistency reasoning methods, proposing Chain-of-Keywords (CoKe) to generate keywords before rationales for rating predictions. The result showed that CoKe-based models reached human-level performance, significantly outperformed GPT-4 with a 2x boost in correlation with human annotators, and required drastically fewer parameters.
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 (CoT) generates free-text explanations that help guide a model's predictions and Self-Consistency (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 $\textbf{C}$hain-$\textbf{o}$f-$\textbf{Ke}$ywords (CoKe), that generates a sequence of keywords $\textit{before}$ 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 number of parameters.