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Naghmeh Farzi · Laura Dietz
Large Language Models (LLMs) are increasingly used as automated judges (LLM judges) to evaluate Information Retrieval (IR) systems, offering a cost-effective complement to human assessments. However, most prior work treats evaluation mainly as a tool for comparison rather than as a signal for imp...
Naghmeh Farzi · Tim Hagen · Eugene Yang · Maik Fröbe · Ronak Pradeep · Hossein A. Rahmani · Xi Wang · Oleg Zendel · Martin Potthast · Laura Dietz
We present the Auto-Judge resource for the meta-evaluation of automated LLM judges, especially judges that evaluate Retrieval-Augmented Generation (RAG) systems that ground their response with citations. The resource couples (i) a data release of topics, pooled RAG responses, and human judgments,...
Laura Dietz · Naghmeh Farzi · Eugene Yang · Dawn Lawrie
Nugget-based LLM judges evaluate Retrieval-Augmented Generation (RAG) systems using a bank of questions that capture the key facts and criteria an answer should address. These nugget banks are typically constructed through a combination of human input and LLM generation. System outputs are graded...