@inproceedings{kumar-nolbaria-2025-bridging,
    title = "Bridging the Data Gap in Financial Sentiment: {LLM}-Driven Augmentation",
    author = "Kumar, Rohit  and
      Nolbaria, Chandan",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-srw.98/",
    pages = "1246--1254",
    ISBN = "979-8-89176-254-1",
    abstract = "Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. This RAG-based augmentation significantly modernizes the data{'}s alignment with current financial language. Furthermore, a robust BERT-BiGRU judge model verifies that the sentiment of the original annotations is faithfully preserved, ensuring the generation of high-quality, temporally relevant, and sentiment-consistent data suitable for advancing FSA model development."
}