arXiv:2605.20730v1 Announce Type: new Abstract: In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks through demonstrations, yet it suffers from escalating inference costs as context length increases. While task vectors offer a promising alternative by compressing demonstrations into compact hidden-state representations, their quality has been evaluated only through downstream task accuracy. This indirect criterion provides limited insight into how to design more effective task vector extraction methods. In this paper, we posit that inference using task vectors s
The rapid advancement of large language models (LLMs) and their increasing deployment in diverse applications necessitates more efficient and cost-effective inference mechanisms.
Improving the efficiency of in-context learning (ICL) via task vectors is critical for scaling LLM applications, reducing operational costs, and expanding the accessibility of advanced AI capabilities.
The proposed method offers a more principled way to design task vectors, potentially leading to significant reductions in computational overhead for high-performance LLMs.
- · Large Language Model Developers
- · Cloud AI Providers
- · AI Application Developers
- · Enterprises Adopting LLMs
- · Companies with inefficient LLM inference infrastructure
More efficient LLM inference will enable faster deployment and lower costs for AI-driven services.
Reduced inference costs could lead to an explosion in new AI applications that were previously economically unfeasible.
This could accelerate the consolidation of AI capabilities among providers who can leverage these efficiencies, further concentrating power in leading AI companies.
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Read at arXiv cs.CL