@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T CYUT at SemEval-2025 Task 6: Prompting with Precision – ESG Analysis via Structured Prompts
%A Wu, Shih -. Hung
%A Lin, Z. h. i. -. H. o. n. g.
%A Lee, Ping -. Hsuan
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wu-etal-2025-cyut
%X 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.
%U https://aclanthology.org/2025.semeval-1.69/
%P 494-501
Markdown (Informal)
[CYUT at SemEval-2025 Task 6: Prompting with Precision – ESG Analysis via Structured Prompts](https://aclanthology.org/2025.semeval-1.69/) (Wu et al., SemEval 2025)
ACL