arXiv:2510.01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples. To clarify and improve these capabilities, we characterize how the statistical properties of the pretraining distribution (e.g., tail behavior, coverage) shape ICL. We develop a theoretical framework that encompasses generalization and task selection and show how distributional properties govern sample efficiency,
This research provides a deeper theoretical understanding of in-context learning in large language models, a key capability whose underlying mechanisms are still being actively explored.
A clearer understanding of how pretraining distributions shape in-context learning can lead to more efficient and effective LLMs, impacting various AI applications and potentially reducing training costs.
This theoretical framework offers new insights into optimizing LLM pretraining strategies to enhance their in-context learning capabilities, shifting from empirical observation to principled design.
- · AI researchers
- · LLM developers
- · Cloud AI providers
- · Inefficient LLM architectures
Improved performance and sample efficiency for large language models, particularly in new tasks.
Reduced computational resources and time required to train highly capable LLMs, lowering barriers to entry for some developers.
Acceleration of AI agent development capabilities due to more robust and adaptable in-context learning.
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Read at arXiv cs.LG