用大模型生成合成数据，提升少样本提示调优效果
Generating Synthetic Datasets for Few-shot Prompt Tuning
- 用分布对齐的生成方法合成与真实数据一致的标签数据
- 在7个句对分类数据集上显著提升少样本提示调优性能
- 合成数据可媲美真实大数据，适合资源有限场景
提示调优的一大局限是依赖大规模标注数据。在少样本学习场景下，提示调优远落后于全模型微调，限制了其应用范围。本文利用强大大模型生成任务特定的标注数据以训练软提示。首先提出分布对齐加权生成调优（DawGen）方法，促使生成数据与少样本真实数据分布一致；随后采用梯度手术方法，在合成数据与真实数据上联合训练软提示，消除不同数据源间的梯度冲突。在7个句对分类数据集上的实验表明，该方法能有效提升少样本学习下的提示调优表现。在QQP、MRPC和SICK数据集上的结果甚至可媲美从大规模真实数据集迁移学习的性能，显示出合成数据作为增强软提示调优的潜力。
原文摘要 · Abstract (English)
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.
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