CYUT at S em E val-2025 Task 6: Prompting with Precision – ESG Analysis via Structured Prompts (2025)

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.

## introduction the concept of environmental, social, and governance (esg) sustainability has emerged as a critical framework for assessing corporate responsibility and long-term viability. as concerns over climate change, social inequality, and governance practices continue to escalate, corporations are increasingly required to demonstrate measurable commitments. however, evaluating these commitments presents significant challenges. traditional assessment methods heavily rely on manual reviews of corporate reports, third-party evaluations, and media sources-approaches that are labor-intensive, costly, difficult to scale, and often inconsistent across regions and languages. to tackle these challenges, our team participated in the semeval-2025 task 6: promiseeval-multinational, multilingual, multi-industry promise verification competition @xcite . this competition introduces a novel multilingual dataset encompassing english, french, chinese, japanese, and korean, designed to assess corporate commitments and their fulfillment in the esg domain. the primary objective is to develop nlp methodologies that automate corporate promise verification by identifying commitments, evaluating supporting evidence, assessing clarity, and inferring appropriate verification timelines. advancements in natural language processing (nlp) have demonstrated immense potential in automating the evaluation of large-scale textual data. early nlp techniques, including sentiment analysis, topic modeling, and named entity recognition, have been widely applied to extract structured insights from esg disclosures. nevertheless, these methods remain constrained by rule-based systems, which struggle to adapt to dynamic and diverse esg datasets. transformer-based models @xcite , such as bert @xcite and @xcite , have revolutionized the field through context-aware text analysis, enhancing the scalability and robustness of nlp applications.(chung and latifi, 2024) evaluated esg-specific pre-trained large language models (llms), such as finbert-esg and finetuned llama models, demonstrating their superior performance over traditional machine learning techniques like svm and xgboost in esg text classification tasks. these models excel at capturing semantic and contextual nuances within esgrelated texts, making them particularly well-suited for analyzing abstract concepts and complex interrelations. despite these advancements, challenges remain in applying nlp techniques to esg evaluation. esg data originate from diverse formats, sources, and languages, necessitating sophisticated approaches capable of integrating both structured and unstructured information. @xcite propose an advanced methodology for processing unstructured esg data, addressing challenges in text extraction, multilingual content, and diverse document formats to improve the accuracy of esg assessments. additionally, many esg indicatorssuch as descriptions of social responsibility initiatives or governance strategies-are inherently qualitative, requiring models to not only extract data but also comprehend and reason about complex relationships. @xcite highlight the difficulties in automating esg scoring using nlp, particularly in handling qualitative esg factors that require contextual reasoning. to address these limitations, this study integrates state-of-the-art nlp techniques, including retrieval-augmented generation (rag) @xcite , chain-of-thought (cot) @xcite , @xcite reasoning, and prompt engineering @xcite , @xcite , to enhance the automation of esg commitment verification. the structured prompt framework systematically guides the model through a multi-stage reasoning process using explicit definitions, clarification rules, concrete examples, constrained label outputs, and stepwise instructions. this design enables the model to accurately comprehend classification standards and make consistent decisions across diverse contexts. by leveraging these methods, our framework provides a multilingual, efficient, and scalable solution that significantly narrows the gap between corporate commitments and measurable outcomes, while enhancing interpretability and reliability in esg evaluations. the climatebert model fine-tuned by @xcite demonstrated an accuracy of 86.34% in assessing greenwashing risks within corporate sustainability reports, underscoring the promising capabilities of language models in the domain of greenwashing detection. by applying cutting-edge nlp methodologies to esg evaluation and testing them within the ml-promise challenge framework, we aim to advance more transparent and reliable corporate esg oversight mechanisms, ultimately fostering sustainable development practices. ## dataset the dataset used in this study is derived from semeval-2025 task 6 @xcite , which focuses on verifying corporate commitments disclosed in environmental, social, and governance (esg) reports. it comprises textual data from multiple companies, annotated with structured labels designed to support the identification and evaluation of corporate pledges and their supporting evidence, thereby facilitating effective esg statement verification through natural language processing (nlp) models. annotations are organized across four principal dimensions: (1) promise status, indicating whether a clear commitment has been made ("yes" or "no"); statistical analysis of the dataset reveals that most corporate statements contain explicit commitments, yet many lack short-term fulfillment targets, potentially undermining their credibility. while documentation often supports pledges, inconsistencies remain due to unverifiable claims and variable evidence quality. practical challenges such as spelling errors, linguistic variation, and unstructured text also impact multilingual model performance. nevertheless, the dataset offers a strong structural foundation for esg commitment verification and highlights directions for future improvements in bias mitigation and model robustness. ## experimental setup to rigorously evaluate the effectiveness of our proposed esg verification framework, we conducted experiments focusing on computational efficiency, model accuracy, and multilingual generalization. this section outlines the technical framework, hardware configuration, retrieval methodology, preprocessing techniques, and evaluation strategy. ## conclusion this study presents an advanced nlp-driven framework for esg commitment verification, addressing the limitations of traditional assessment methods. by leveraging retrieval-augmented generation, chain-of-thought reasoning, and structured prompt engineering, our approach enhances the automation, accuracy, and interpretability of esg evaluations. the experimental results from our participation in the semeval-2025 promiseeval task validate the effectiveness of our model, demonstrating its superior performance in classifying corporate commitments, verifying evidence, and assess-ing the credibility of esg-related claims. future research could explore further improvements in multilingual adaptability and the integration of external knowledge sources to enhance contextual understanding. ultimately, our methodology contributes to the development of scalable, reliable, and transparent esg verification systems, supporting global efforts in corporate sustainability assessment.