arXiv:2605.03644v2 Announce Type: replace Abstract: Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Language Models (LLMs). However, existing methods typically rely on a predetermined, fixed number of shots. This static approach often fails to adapt to the varying difficulty of different queries, leading to either insufficient context or interference from noise. Furthermore, the prohibitive computational and memory costs of long contexts severely limit Many-Shot's feasibility. To address the above
Development of more efficient in-context learning methods is critical as LLMs scale and their computational demands become a bottleneck for wider application.
This paper addresses a fundamental constraint in the scalability and practical application of large language models, potentially making powerful ICL more accessible and cost-effective.
The ability to run many-shot ICL more efficiently by adaptively selecting examples and reusing KV cache significantly reduces computational cost and memory, enabling broader deployment.
- · AI developers
- · Cloud computing providers
- · Businesses leveraging LLMs
- · Researchers in NLP
- · Companies with inefficient LLM deployments
- · Cloud providers unable to optimize LLM inference
More sophisticated and context-aware AI applications become economically viable.
Reduced operational costs for LLMs could accelerate their integration into various industries, driving adoption and innovation.
This efficiency gain might lower the barrier to entry for developing powerful AI, potentially democratizing access to advanced AI capabilities.
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Read at arXiv cs.AI