@inproceedings{wu-etal-2025-cyut,
    title = "{CYUT} at {S}em{E}val-2025 Task 6: Prompting with Precision {--} {ESG} Analysis via Structured Prompts",
    author = "Wu, Shih - Hung  and
      Lin, Z h i - H o n g  and
      Lee, Ping - Hsuan",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.69/",
    pages = "494--501",
    ISBN = "979-8-89176-273-2",
    abstract = "In response to the increasing need for efficientESG verification, we propose an innovativeNLP framework that automates the evaluationof corporate sustainability claims. Ourmethod integrates Retrieval-Augmented Generation,Chain-of-Thought reasoning, and structuredprompt engineering to effectively processand classify diverse, multilingual ESG disclosures.Evaluated under the SemEval-2025PromiseEval competition, our system achievedtop-tier performance{---}securing first place onthe public English leaderboard, excelling in theFrench track, and delivering marked improvementsover conventional machine learning approaches.These results highlight the framework{'}spotential to offer a scalable, transparent,and robust solution for corporate ESG assessment."
}