Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-Context Learning (ICL) can only use a small number of supervised examples due to context length limits. In this paper, we propose Super In-Context Learning (SuperICL) which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. Our experiments demonstrate that SuperICL can improve performance beyond state-of-the-art fine-tuned models while addressing the instability problem of in-context learning. Furthermore, SuperICL can enhance the capabilities of smaller models, such as multilinguality and interpretability.
@article{arxiv.2305.08848,
title = {Small Models are Valuable Plug-ins for Large Language Models},
author = {Canwen Xu and Yichong Xu and Shuohang Wang and Yang Liu and Chenguang Zhu and Julian McAuley},
journal= {arXiv preprint arXiv:2305.08848},
year = {2023}
}