SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation

• URL: http://arxiv.org/abs/2412.10906v1
• Date: Sat, 14 Dec 2024 17:30:33 GMT
• Title: SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
• Authors: Qilong Wu, Xiaoneng Xiang, Hejia Huang, Xuan Wang, Yeo Wei Jie, Ranjan Satapathy, Ricardo Shirota Filho, Bharadwaj Veeravalli,
• Abstract summary: SusGen-30K is a category-balanced dataset comprising seven financial NLP tasks and ESG report generation. We developed SusGen-GPT, a suite of models achieving state-of-the-art performance across six adapted and two off-the-shelf tasks. Based on this, we propose the SusGen system, integrated with Retrieval-Augmented Generation (RAG) to assist in sustainability report generation.
• Score: 8.400304053291938
• License:
• Abstract: The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open-source LLMs proficient in both finance and ESG domains remain scarce. To address this gap, we introduce SusGen-30K, a category-balanced dataset comprising seven financial NLP tasks and ESG report generation, and propose TCFD-Bench, a benchmark for evaluating sustainability report generation. Leveraging this dataset, we developed SusGen-GPT, a suite of models achieving state-of-the-art performance across six adapted and two off-the-shelf tasks, trailing GPT-4 by only 2% despite using 7-8B parameters compared to GPT-4's 1,700B. Based on this, we propose the SusGen system, integrated with Retrieval-Augmented Generation (RAG), to assist in sustainability report generation. This work demonstrates the efficiency of our approach, advancing research in finance and ESG.

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