Showing 2025Show all
3 papers · 1 filter
cs.IR2025
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…
cs.CL2025
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…
cs.IR2025
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…