ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization
Suyoung Bae, CheolWon Na, Jaehoon Lee, Yumin Lee, YunSeok Choi, Jee-Hyong Lee
Abstract
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 reference-free 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.- Anthology ID:
- 2026.acl-long.653
- Volume:
- Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14352–14377
- Language:
- URL:
- https://preview.aclanthology.org/ingest-acl/2026.acl-long.653/
- DOI:
- Cite (ACL):
- Suyoung Bae, CheolWon Na, Jaehoon Lee, Yumin Lee, YunSeok Choi, and Jee-Hyong Lee. 2026. ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14352–14377, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization (Bae et al., ACL 2026)
- PDF:
- https://preview.aclanthology.org/ingest-acl/2026.acl-long.653.pdf