用神经网络自动选最优数据集组合，提升大模型多任务学习效果。
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
- 用神经网络预测最佳数据集组合，迭代优化选择过程。
- 在12个生物医学数据集上验证，4类任务均表现更优。
- 不依赖具体模型或领域，适合广泛场景的多任务训练。
为高效选择提升大语言模型多任务学习性能的最佳数据集组合，我们提出一种新框架，利用神经网络预测最优组合。该框架迭代优化选择过程，显著提升效率，且与模型、数据集和领域无关。在四个任务（命名实体识别、关系抽取、事件抽取、文本分类）的12个生物医学数据集上的实验表明，该方法能有效识别出优于人类直觉的组合，即使对看似无前景的任务亦然。结果验证了该框架在最大化多任务学习潜力方面的可行性。
原文摘要 · Abstract (English)
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.
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