Summary of Generating Synthetic Datasets For Few-shot Prompt Tuning, by Xu Guo et al.
Generating Synthetic Datasets for Few-shot Prompt Tuning
by Xu Guo, Zilin Du, Boyang Li, Chunyan Miao
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper proposes a novel approach to improve prompt tuning in few-shot learning settings. The method leverages powerful Large Language Models (LLMs) to synthesize task-specific labeled data for training soft prompts. The authors introduce a distribution-aligned weighted generator tuning (DawGen) method to generate in-distribution data that aligns with real data, and then train soft prompts on both synthetic and real datasets using gradient surgery. The proposed method is tested on seven sentence-pair classification datasets, including QQP, MRPC, and SICK, demonstrating its effectiveness in boosting prompt tuning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to improve learning with small amounts of labeled data. Scientists use powerful computers to generate fake labeled data that’s similar to real data, and then train special prompts on this fake data and the real data together. This helps the computer learn better from small datasets. The results show that this method is just as good as using a lot of labeled data to train models. |
Keywords
» Artificial intelligence » Boosting » Classification » Few shot » Prompt
