Go-tuning: Improving Zero-shot Learning Abilities Of Smaller Language Models

Xu Jingjing, Dong Qingxiu, Liu Hongyi, Li Lei. Arxiv 2022

[Paper]    
BERT GPT Language Modeling Masked Language Model Model Architecture Pretraining Methods Prompting RAG Training Techniques

With increasing scale, large language models demonstrate both quantitative improvement and new qualitative capabilities, especially as zero-shot learners, like GPT-3. However, these results rely heavily on delicate prompt design and large computation. In this work, we explore whether the strong zero-shot ability could be achieved at a smaller model scale without any external supervised data. To achieve this goal, we revisit masked language modeling and present a geometry-guided self-supervised learning method (Go-tuningfor short) by taking a small number of task-aware self-supervised data to update language models further. Experiments show that Go-tuning can enable T5-small (80M) competitive zero-shot results compared with large language models, such as T5-XL (3B). We also apply Go-tuning on multi-task settings and develop a multi-task model, mgo-T5 (250M). It can reach the average performance of OPT (175B) on 9 datasets.

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