@inproceedings{zhan-zhang-2025-towards,
    title = "Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models",
    author = "Zhan, Zaifu  and
      Zhang, Rui",
    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/ingest-emnlp/2025.findings-naacl.297/",
    doi = "10.18653/v1/2025.findings-naacl.297",
    pages = "5373--5386",
    ISBN = "979-8-89176-195-7",
    abstract = "To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neural network to predict the best dataset combinations. The framework iteratively refines the selection, greatly improving efficiency, while being model-, dataset-, and domain-independent. Through experiments on 12 biomedical datasets across four tasks{---}named entity recognition, relation extraction, event extraction, and text classification{---}we demonstrate that our approach effectively identifies better combinations, even for tasks that may seem unpromising from a human perspective. This verifies that our framework provides a promising solution for maximizing MTL potential."
}