3 papers
cs.CL2026
RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
Fabian Ridder, Laurin Lessel, Malte Schilling
Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or domain-specific knowledge. Non…
cs.CL2024
The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States
Fabian Ridder, Malte Schilling
Detecting hallucinations in large language models (LLMs) is critical for enhancing their reliability and trustworthiness. Most research focuses on hallucinations as deviations from…
cs.CL2024
Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course
Sebastian Kahl, Felix Löffler, Martin Maciol +6
This study evaluates the performance of Large Language Models (LLMs) as an Artificial Intelligence-based tutor for a university course. In particular, different advanced techniques…