Summary of On Unsupervised Prompt Learning For Classification with Black-box Language Models, by Zhen-yu Zhang et al.
On Unsupervised Prompt Learning for Classification with Black-box Language Models
by Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama
First submitted to arxiv on: 4 Oct 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 The paper proposes a novel approach to fine-tune large language models (LLMs) with unlabeled data, enabling more efficient and accurate classification. By modeling the prompt as a sequence of discrete tokens with learnable categorical distributions, the authors demonstrate unsupervised prompt learning for classification with black-box LLMs. This method leverages in-context learning capabilities, allowing reliable pseudo-labeled data to serve as demonstrations alongside the prompt. The proposed algorithm is evaluated on benchmark datasets, showcasing its effectiveness. This technique has significant implications for real-world applications, enabling owners of black-box LLMs to fine-tune their models without requiring labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to use large language models (LLMs) with lots of text information that doesn’t have labels. Usually, you need labeled data to make the model work better for specific tasks. But what if the model could label the text itself just as well as humans? This idea led researchers to develop an unsupervised method to fine-tune LLMs using unlabeled data. The approach uses something called prompts, which are like secret messages that help the model learn and make predictions. The authors tested their method on several datasets and found it works really well. |
Keywords
» Artificial intelligence » Classification » Prompt » Unsupervised
