Generating Synthetic Datasets for Few-shot Prompt Tuning
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917802404741120 |
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| author | Guo, Xu Du, Zilin Li, Boyang Miao, Chunyan |
| author_facet | Guo, Xu Du, Zilin Li, Boyang Miao, Chunyan |
| contents | A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, limiting its scope of application. In this paper, we leverage the powerful LLMs to synthesize task-specific labeled data for training the soft prompts. We first introduce a distribution-aligned weighted generator tuning (DawGen) method to encourage generating in-distribution data that aligns with the few-shot real data. Then, we train soft prompts on both synthetic and real datasets using a gradient surgery approach, which eliminates the conflicting gradients from different data sources. Experiments on seven sentence-pair classification datasets demonstrate the effectiveness of our proposed method for boosting prompt tuning in few-shot learning settings. Results on QQP, MRPC, and SICK datasets are even comparable to the performance of transfer learning from large real-world datasets, showing the promise of synthetic data as an alternative for enhancing soft prompt tuning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating Synthetic Datasets for Few-shot Prompt Tuning Guo, Xu Du, Zilin Li, Boyang Miao, Chunyan Computation and Language Artificial Intelligence A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, limiting its scope of application. In this paper, we leverage the powerful LLMs to synthesize task-specific labeled data for training the soft prompts. We first introduce a distribution-aligned weighted generator tuning (DawGen) method to encourage generating in-distribution data that aligns with the few-shot real data. Then, we train soft prompts on both synthetic and real datasets using a gradient surgery approach, which eliminates the conflicting gradients from different data sources. Experiments on seven sentence-pair classification datasets demonstrate the effectiveness of our proposed method for boosting prompt tuning in few-shot learning settings. Results on QQP, MRPC, and SICK datasets are even comparable to the performance of transfer learning from large real-world datasets, showing the promise of synthetic data as an alternative for enhancing soft prompt tuning. |
| title | Generating Synthetic Datasets for Few-shot Prompt Tuning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.10865 |