arXiv:2605.24981v1 Announce Type: new Abstract: Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. To address this challenge, we develop SELECT-LLM, the first framework for active model selection of LLMs. SELECT-LLM aims to find a small set of queries whose annotations are most informative for identifying the best LLM for a given task. To this end, we introduce a query selection rule based on expected information gain, computed from pairwise similarities between candi
The proliferation of powerful LLMs from various providers is creating a critical need for efficient and cost-effective methods to select the optimal model for specific applications, moving beyond expensive, manual annotation processes.
This development addresses a significant bottleneck in enterprise LLM adoption, allowing organizations to more rapidly and affordably identify suitable AI solutions, thereby accelerating deployment and value creation.
The ability to actively select LLMs with limited annotations shifts the paradigm from costly, fixed-set evaluations to more dynamic, information-gain-driven approaches, reducing the barrier to entry for LLM integration.
- · Enterprises adopting LLMs
- · LLM developers (by improving feedback loops)
- · AI consultants and integrators
- · Data annotation platform providers (those adapting to active learning)
- · Traditional, manual annotation services (if they don't adapt)
- · Organizations slow to adopt active learning methodologies
- · Inefficient LLM evaluation frameworks
More rapid and cost-effective deployment of LLM solutions across various industries.
Increased competition among LLM providers as selection becomes more data-driven and less reliant on brand or general perception.
The development of more specialized and hyper-optimized LLMs for niche tasks, driven by efficient feedback loops.
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