@inproceedings{wu-etal-2025-susgen,
    title = "$SusGen-GPT$: A Data-Centric {LLM} for Financial {NLP} and Sustainability Report Generation",
    author = "Wu, Qilong  and
      Xiang, Xiaoneng  and
      Hejia, Huang  and
      Wang, Xuan  and
      Wei Jie, Yeo  and
      Satapathy, Ranjan  and
      Filho, Ricardo Shirota  and
      Veeravalli, Bharadwaj",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.66/",
    pages = "1184--1203",
    ISBN = "979-8-89176-195-7",
    abstract = "The rapid growth of the financial sector and the increasing focus on Environmental, Social, and Governance (ESG) considerations have created a pressing need for advanced natural language processing (NLP) tools. Despite recent advancements, there is still a notable absence of open-source Large Language Models (LLMs) that are proficient across both general finance and ESG domains, such as generating ESG reports. To address this gap, we introduce $SusGen$-$30k$, a high-quality, category-balanced dataset comprising seven financial NLP tasks. In addition, we propose $TCFD$-$Bench$, a benchmark designed to improve the evaluation of sustainability report generation. Our data-centric approach led to the development of a suite of models, $SusGen$-$GPT$, trained on the curated dataset. These models were evaluated across six adapted tasks and two off-the-shelf tasks, showing state-of-the-art performance, surpassing all other models except GPT-4. Remarkably, $SusGen$-$GPT$ achieved an average score only 0.02 below GPT-4, despite using models with only 7-8B parameters compared to much larger GPT-4. This demonstrates the efficiency of our approach in delivering high performance with significantly fewer resources, addressing existing challenges and fostering further advancements in the financial and ESG research community."
}