用小模型实现金融与可持续报告生成的顶尖表现
SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
- 基于7-8B参数构建金融与ESG专用大模型
- 在6项任务上达顶尖水平，仅比GPT-4低2%
- 适合金融与可持续发展领域研究者使用
金融行业快速发展及对环境、社会和治理（ESG）关注的提升，凸显了先进自然语言处理工具的需求。然而，兼具金融与ESG能力的开源大模型仍十分稀缺。为此，我们构建了SusGen-30K数据集，涵盖七类金融NLP任务及可持续报告生成，并提出TCFD-Bench基准用于评估可持续报告生成。基于该数据集，我们开发了SusGen-GPT系列模型，在六项适配任务和两项现成任务中均达到领先性能，仅以7-8B参数实现的效果，相较拥有1,700B参数的GPT-4仅低2%。在此基础上，我们提出结合检索增强生成（RAG）的SusGen系统，助力可持续报告生成。本工作验证了方法的高效性，推动了金融与可持续领域的研究进展。
原文摘要 · Abstract (English)
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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