@inproceedings{huang-etal-2025-multilingual-promise,
    title = "Multilingual Promise Verification in {ESG} Reports with Large Language Model Performance Evaluation",
    author = "Huang, Wei-Chen  and
      Lu, Hsin-Ting  and
      Chen, Wen-Ze  and
      Day, Min-Yuh",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2025.rocling-main.32/",
    pages = "303--313",
    ISBN = "979-8-89176-379-1",
    abstract = "Corporate ESG reports often contain statements that are vague or difficult to verify, creating room for potential greenwashing. Building automated systems to evaluate such claims is therefore a relevant research direction. Yet, existing analytical tools still show limited ability to verify sustainability promises in multiple languages, especially beyond English. This study examines how large language models (GPT-5) perform in verifying ESG-related promises across Chinese, Japanese, and English reports, aiming to provide a multilingual evaluation baseline. We assess four verification tasks using the PromiseEval datasets [1] in three languages, comparing five prompting strategies from zero-shot to five-shot learning, including Chain-of-Thought reasoning. The four subtasks are Promise Identification (PI), Evidence Status Assessment (ESA), Evidence Quality Evaluation (EQE), and Verification Timeline Prediction (VTP). The five-shot setting achieved the highest overall performance (71.12 {\%} accuracy, 51.92 {\%} Macro-F1). Although the accuracy results appear higher for Chinese (85.12 {\%}) than for Japanese (68.94 {\%}) and English (63.62 {\%}), this mainly reflects class imbalance in the data. Hence, Macro-F1 provides a fairer comparison across languages. Among the four tasks, Evidence Quality Evaluation (EQE) remains the most difficult. While Chain-of-Thought prompting slightly lowers the overall average, it shows selective benefit on the more complex EQE task. Overall, this work offers a clearer multilingual baseline for ESG promise verification and supports the development of language-based tools that enhance the credibility and transparency of sustainability reporting."
}