New theory explains LLM in-context learning beyond pretraining data

A new theoretical framework has been developed to explain how In-Context Learning (ICL) in large language models can succeed even when instructions or target tasks are outside the model's pretraining data. The framework distinguishes between shifts in instructions and shifts in target tasks, showing how demonstrations can help identify tasks and reduce sensitivity to prompt templates. It also explains how ICL can handle long-horizon tasks by decomposing them into smaller, pretraining-supported subtasks, thereby avoiding direct selection of unsupported complete tasks. AI

IMPACT Provides a theoretical basis for understanding and potentially improving LLM capabilities in handling novel instructions and complex tasks.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for LLM in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• In-Context Learning
• Hugging Face
• LLMs

• paper
• model release

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