As Large Language Models (LLMs) have become capable of generating long and descriptive code summaries, accurate and reliable evaluation of factual consistency has become a critical challenge.However, previous evaluation methods are primarily designed for short summaries of isolated code snippets.Consequently, they struggle to provide fine-grained evaluation of multi-sentence functionalities and fail to accurately assess dependency context commonly found in real-world code summaries.To address this, we propose ReFEree, a referencefree and fine-grained method for evaluating factual consistency in real-world code summaries.We define factual inconsistency criteria specific to code summaries and evaluate them at the segment level using these criteria along with dependency information.These segment-level results are then aggregated into a fine-grained score.We construct a code summarization benchmark with human-annotated factual consistency labels.The evaluation results demonstrate that ReFEree achieves the highest correlation with human judgment among 13 baselines, improving 15-18% over the previous state-of-the-art.Our code and data are available at https: //github.com/bsy99615/ReFEree.git.

This paper introduces CharMoral, a dataset designed to analyze the moral evolution of characters in long-form narratives. CharMoral, built from 1,337 movie synopses, includes annotations for character actions, context, and morality labels. To automatically construct CharMoral, we propose a four-stage framework, utilizing Large Language Models, to automatically classify actions as moral or immoral based on context. Human evaluations and various experiments confirm the framework’s effectiveness in moral reasoning tasks in multiple genres. Our code and the CharMoral dataset are publicly available at https://github.com/BaeSuyoung/CharMoral.

Suyoung Bae, YunSeok Choi, Hyojun Kim, Jee-Hyong Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Suyoung Bae, YunSeok Choi, Jee-Hyong Lee. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.