arXiv:2509.15676v2 Announce Type: replace Abstract: In-context learning (ICL) has emerged as a powerful paradigm for adapting large language models (LLMs) to new and data-scarce tasks using only a few carefully selected task-specific examples presented in the prompt. However, given the limited context size of LLMs, a fundamental question arises: Which examples should be selected to maximize performance on a given user query? While nearest-neighbor-based methods like KATE have been widely adopted for this purpose, they suffer from well-known drawbacks in high-dimensional embedding spaces, inclu
The paper addresses a critical limitation in in-context learning (ICL) optimization as Large Language Models become more widely adopted with increasing context windows, making example selection crucial.
Improving ICL efficiency by selecting optimal examples directly enhances LLM performance and reduces computational overhead, broadening the applicability of LLMs to data-scarce tasks.
The ability to more effectively select in-context examples will lead to more robust and higher-performing LLM applications, particularly for fine-tuning and specialized use cases.
- · AI developers
- · LLM application providers
- · Data-scarce industries
- · Inefficient prompt engineering methods
- · LLM users unwilling to curate data
Enhanced performance and reliability of LLM-powered applications across various domains.
Increased adoption of LLMs in niche or specialized fields due to improved data efficiency and adaptability.
A potential shift in how data is curated and prepared for LLM training and deployment, emphasizing quality over raw quantity for in-context examples.
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Read at arXiv cs.LG