EaSyGuide : ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models

TL;DR

This paper introduces EaSyGuide, a framework leveraging large language models for multi-lingual ESG issue classification in news articles, achieving top results in the FinNLP-2023 shared task.

Contribution

It presents a novel approach combining multiple language models and augmentation techniques for effective ESG issue identification across languages.

Findings

Achieved first place in English subtask with F1-score 0.69

Secured second place in French subtask with F1-score 0.78

Demonstrated the effectiveness of large language models in ESG classification

Abstract

This paper presents our participation in the FinNLP-2023 shared task on multi-lingual environmental, social, and corporate governance issue identification (ML-ESG). The task's objective is to classify news articles based on the 35 ESG key issues defined by the MSCI ESG rating guidelines. Our approach focuses on the English and French subtasks, employing the CerebrasGPT, OPT, and Pythia models, along with the zero-shot and GPT3Mix Augmentation techniques. We utilize various encoder models, such as RoBERTa, DeBERTa, and FinBERT, subjecting them to knowledge distillation and additional training. Our approach yielded exceptional results, securing the first position in the English text subtask with F1-score 0.69 and the second position in the French text subtask with F1-score 0.78. These outcomes underscore the effectiveness of our methodology in identifying ESG issues in news articles across different languages. Our findings contribute to the exploration of ESG topics and highlight the potential of leveraging advanced language models for ESG issue identification.