5 papers
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-…
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…
Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMs
Samuel Frontull, Thomas Ströhle
Large Language Models (LLMs) have demonstrated strong capabilities in multilingual machine translation, sometimes even outperforming traditional neural systems. However, previous r…
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…
Vision Transformers for Weakly-Supervised Microorganism Enumeration
Javier Ureña Santiago, Thomas Ströhle, Antonio RodrÃguez-Sánchez +1
Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. Howeve…