AI RESEARCH
Large Language Model Selection with Limited Annotations
arXiv CS.LG
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ArXi:2605.24981v1 Announce Type: cross 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