arXiv:2606.28926v1 Announce Type: cross Abstract: In-context learning (ICL) is an emerging paradigm that employs the semantic information inherent in large language models (LLMs) for generating answers to user queries. While the remarkable performance of ICL has been widely known, a general modeling and a rigorous theoretical analysis of this paradigm are still lacking. This work presents a probabilistic model for ICL and derives the performance of ICL for both general parametric distributions and exponential families. Based on the derived results, the work explains the impact of multiple fact
The rapid advancement and widespread application of large language models have created an urgent need for deeper theoretical understanding of their core mechanisms, such as in-context learning.
A rigorous theoretical framework for in-context learning could unlock substantial improvements in LLM design, predictability, and efficiency, moving beyond empirical trial and error.
The ability to formally model and predict the performance of in-context learning provides a basis for more principled LLM development rather than purely black-box experimentation.
- · AI researchers
- · LLM developers
- · AI-driven industries
- · Machine learning theoreticians
- · Empirical AI development methodologies
Improved understanding of LLM behavior leads to more robust and reliable AI systems.
This foundational insight could accelerate the development of more complex and autonomous AI agents.
Enhanced control over LLM capabilities might lead to new paradigms in human-AI interaction and knowledge creation.
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Read at arXiv cs.LG