Small Models are Valuable Plug-ins for Large Language Models

Understand

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.