Large Language Model Selection with Limited Annotations
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 candidate model outputs. Because this rule only uses generated model responses, SELECT-LLM can be applied across candidate models without assumptions ...
| Authors | Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 25 September 2026 |
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