@inproceedings{chaudhuri-etal-2025-llmforum,
    title = "{LLMF}orum-{RAG}: A Multilingual, Multi-domain Framework for Factual Reasoning via Weighted Retrieval and {LLM} Collaboration",
    author = "Chaudhuri, Soham  and
      Saha, Dipanjan  and
      Das, Dipankar",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.88/",
    pages = "1426--1431",
    ISBN = "979-8-89176-303-6",
    abstract = "LLMs have emerged as a transformative technology, enabling a wide range of tasks such as text generation, summarization, question answering, and more. The use of RAG with LLM is on the rise to provide deeper knowledge bases of various domains. In the present study, we propose a RAG framework that employs weighted Rocchio mechanism for retrieval and LLM collaborative forum with supervision for generation. Our framework is evaluated in two downstream tasks: a biomedical question answering (BioASQ-QA) and a multilingual claim verification (e.g. in English, Hindi, and Bengali) to showcase its adaptability across various domains and languages. The proposed retriever is capable to achieve substantial improvement over BM25 of $+8\%$ (BioASQ-QA), $+15\%$ (English), $+5\%$ (Hindi), and $+20\%$ (Bengali) for Recall@5. In veracity classification, our framework achieves an average answer correctness of 0.78 on BioASQ-QA while achieving F1-score of 0.59, 0.56, and 0.41 for English, Hindi and Bengali languages, respectively. These results demonstrate the effectiveness and robustness of our framework for retrieval and generation in multilingual and multi-domain settings."
}