用大模型直接标注目标数据，免训练就能提升跨域分类效果。
Similarity-Based Domain Adaptation with LLMs
- 利用大模型对目标数据进行直接标注，跳过源域模型训练。
- 在跨域文本分类上比当前最佳方法高2.44%准确率。
- 适合快速部署且源数据多变的现实场景应用。
无监督领域自适应利用多个源域的丰富标注数据，以泛化到未标注的目标域数据。以往研究主要关注在源域和目标域之间学习领域不变特征，但这些方法通常需要使用源域数据训练模型，耗时且限制了在不同源数据场景下的应用。本文提出一种简单框架，利用大语言模型（LLMs）的强大泛化能力对目标数据进行标注，无需源模型训练，随后引入一种新颖的基于相似性的知识蒸馏损失。在跨域文本分类任务上的大量实验表明，该框架表现优异，相较于当前最优方法准确率提升2.44%。
原文摘要 · Abstract (English)
Unsupervised domain adaptation leverages abundant labeled data from various source domains to generalize onto unlabeled target data. Prior research has primarily focused on learning domain-invariant features across the source and target domains. However, these methods often require training a model using source domain data, which is time-consuming and can limit model usage for applications with different source data. This paper introduces a simple framework that utilizes the impressive generalization capabilities of Large Language Models (LLMs) for target data annotation without the need of source model training, followed by a novel similarity-based knowledge distillation loss. Our extensive experiments on cross-domain text classification reveal that our framework achieves impressive performance, specifically, 2.44\% accuracy improvement when compared to the SOTA method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。