5 citations · 5 across the 4 of their papers we have counts for
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Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies
Lorenz Brehme, Thomas Ströhle, Ruth Breu
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge to answer questions more accurately. However, research on evaluating RAG systems-…
RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
Lorenz Brehme, Benedikt Dornauer, Jan-Henrik Böttcher +3
Evaluating Retrieval-Augmented Generation (RAG) systems using static multi-turn datasets fails to capture the dynamic nature of real-world dialogues. Existing evaluation methods re…
Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
Lorenz Brehme, Benedikt Dornauer, Thomas Ströhle +2
Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant informati…
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets
Lorenz Brehme, Thomas Ströhle, Ruth Breu
Retrieval-Augmented Generation (RAG) has advanced significantly in recent years. The complexity of RAG systems, which involve multiple components-such as indexing, retrieval, and g…