3 papers
cs.CL2026
Large Language Model Selection with Limited Annotations
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
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. T…
cs.CL2025
Active Model Selection for Large Language Models
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
We introduce LLM SELECTOR, the first framework for active model selection of Large Language Models (LLMs). Unlike prior evaluation and benchmarking approaches that rely on fully an…
cs.CL2025
A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
Karahan SarıtaÅ, Kıvanç Tezören, Yavuz Durmazkeser
In recent years, evaluating the Theory of Mind (ToM) capabilities of large language models (LLMs) has received significant attention within the research community. As the field rap…