Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
Zaifu Zhan, Rui Zhang · 2025
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.