FuseGen: PLM Fusion for Data-generation based Zero-shot Learning

arXiv:2406.12527v1 Announce Type: new
Abstract: Data generation-based zero-shot learning, although effective in training Small Task-specific Models (STMs) via synthetic datasets generated by Pre-trained Language Models (PLMs), is often limited by the low quality of such synthetic datasets. Previous solutions have primarily focused on single PLM settings, where synthetic datasets are typically restricted to specific sub-spaces and often deviate from real-world distributions, leading to severe distribution bias. To mitigate such bias, we propose FuseGen, a novel data generation-based zero-shot learning framework …

arxiv cs.cl data fusion type zero-shot