SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913613260783616 |
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| author | Wu, Qilong Xiang, Xiaoneng Huang, Hejia Wang, Xuan Jie, Yeo Wei Satapathy, Ranjan Filho, Ricardo Shirota Veeravalli, Bharadwaj |
| author_facet | Wu, Qilong Xiang, Xiaoneng Huang, Hejia Wang, Xuan Jie, Yeo Wei Satapathy, Ranjan Filho, Ricardo Shirota Veeravalli, Bharadwaj |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10906 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation Wu, Qilong Xiang, Xiaoneng Huang, Hejia Wang, Xuan Jie, Yeo Wei Satapathy, Ranjan Filho, Ricardo Shirota Veeravalli, Bharadwaj Computation and Language Computational Engineering, Finance, and Science Machine Learning Computational Finance 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. |
| title | SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation |
| topic | Computation and Language Computational Engineering, Finance, and Science Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2412.10906 |