Summary of Susgen-gpt: a Data-centric Llm For Financial Nlp and Sustainability Report Generation, by Qilong Wu et al.
SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
by Qilong Wu, Xiaoneng Xiang, Hejia Huang, Xuan Wang, Yeo Wei Jie, Ranjan Satapathy, Ricardo Shirota Filho, Bharadwaj Veeravalli
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Computational Finance (q-fin.CP)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SusGen-30K, a category-balanced dataset for financial NLP tasks and ESG report generation, along with TCFD-Bench, a benchmark for evaluating sustainability report generation. The authors develop SusGen-GPT, a suite of models achieving state-of-the-art performance across eight adapted and two off-the-shelf tasks, leveraging the new dataset. This work demonstrates the efficiency of the approach, advancing research in finance and ESG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special tool for understanding financial information better. It makes a big collection of examples and tests them to see how well they can do certain jobs. The results are really good, which means this tool could help people make more informed decisions about investing or business. It’s also important for the environment and society. |
Keywords
» Artificial intelligence » Gpt » Nlp
