Auto-ICL: In-Context Learning without Human Supervision
Jinghan Yang Shuming Ma Furu Wei
In the era of Large Language Models (LLMs), human-computer interaction has
evolved towards natural language, offering unprecedented flexibility. Despite
this, LLMs are heavily reliant on well-structured prompts to function
efficiently within the realm of In-Context Learning. Vanilla In-Context
Learning relies on human-provided contexts, such as labeled examples, explicit
instructions, or other guiding mechanisms that shape the model's outputs. To
address this challenge, our study presents a universal framework named
Automatic In-Context Learning. Upon receiving a user's request, we ask the
model to independently generate examples, including labels, instructions, or
reasoning pathways. The model then leverages this self-produced context to
tackle the given problem. Our approach is universally adaptable and can be
implemented in any setting where vanilla In-Context Learning is applicable. We
demonstrate that our method yields strong performance across a range of tasks,
standing up well when compared to existing methods.