Summary of Zero-to-strong Generalization: Eliciting Strong Capabilities Of Large Language Models Iteratively Without Gold Labels, by Chaoqun Liu et al.
Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels
by Chaoqun Liu, Qin Chao, Wenxuan Zhang, Xiaobao Wu, Boyang Li, Anh Tuan Luu, Lidong Bing
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have shown impressive performance through supervised fine-tuning or in-context learning using gold labels. However, this approach is limited by the availability of gold labels. This study explores whether LLMs can perform well solely using unlabeled data, a scenario where humans cannot provide such labels. The researchers propose a new paradigm called zero-to-strong generalization, which iteratively prompts LLMs to annotate unlabeled data and retain high-quality labels by filtering. Surprisingly, this process gradually unlocks LLMs’ potential on downstream tasks. Experiments on extensive classification and reasoning tasks confirm the effectiveness of this framework. Analysis shows that it is effective for both in-context learning and fine-tuning, and for various model sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really smart computers that can do lots of things. But right now, they need people to help them learn by giving them labels or clues. What if we could teach these models without needing human help? This study tries to figure out how to make these models learn better just using regular data, without any special labels. They found a way to make the models get smarter and smarter as they learned from this data. It works for lots of different tasks, like classifying pictures or answering questions. This is important because it could help us use these powerful computers to do even more things. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Generalization » Supervised
