不用标注数据，迭代让大模型自动生成高质量标签并提升能力
Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels
- 用自生成标签迭代优化模型，不依赖人工标注
- 在多个任务上实现从零到强的泛化性能
- 适合无标注数据、复杂任务场景，支持各类模型大小
大语言模型（LLMs）通过有监督微调或上下文学习使用黄金标注展现出优异性能。然而，该范式受限于黄金标注的可用性，尤其在人类难以提供标注的复杂任务中。本文探索仅利用未标注数据是否可激发模型强能力，提出一种新范式——零到强泛化。通过迭代提示模型对未标注数据进行标注，并基于过滤保留高质量标签，我们发现该过程能逐步释放模型在下游任务中的潜力。在广泛分类与推理任务上的实验验证了该框架的有效性。分析表明，该范式对上下文学习和微调均有效，适用于不同规模的模型。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by the availability of gold labels, while in certain scenarios, LLMs may need to perform tasks that are too complex for humans to provide such labels. To tackle this challenge, this study explores whether solely utilizing unlabeled data can elicit strong model capabilities. We propose a new paradigm termed zero-to-strong generalization. We iteratively prompt LLMs to annotate unlabeled data and retain high-quality labels by filtering. Surprisingly, we obverse that this iterative process gradually unlocks LLMs' potential on downstream tasks. Our experiments on extensive classification and reasoning tasks confirm the effectiveness of our proposed framework. Our analysis indicates that this paradigm is effective for both in-context learning and fine-tuning, and for various model sizes.
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