13 papers · 1 filter
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
Juraj Vladika, Annika Domres, Mai Nguyen +10
Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and reg…
Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models
Juraj Vladika, Mahdi Dhaini, Florian Matthes
The growing capabilities of Large Language Models (LLMs) show significant potential to enhance healthcare by assisting medical researchers and physicians. However, their reliance o…
Correcting Hallucinations in News Summaries: Exploration of Self-Correcting LLM Methods with External Knowledge
Juraj Vladika, Ihsan Soydemir, Florian Matthes
While large language models (LLMs) have shown remarkable capabilities to generate coherent text, they suffer from the issue of hallucinations -- factually inaccurate statements. Am…
Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning
Juraj Vladika, Ivana Hacajová, Florian Matthes
Fact verification (FV) aims to assess the veracity of a claim based on relevant evidence. The traditional approach for automated FV includes a three-part pipeline relying on short…
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems
Juraj Vladika, Florian Matthes
Retrieval-augmented generation (RAG) has emerged as an approach to augment large language models (LLMs) by reducing their reliance on static knowledge and improving answer factuali…
Enhancing Answer Attribution for Faithful Text Generation with Large Language Models
Juraj Vladika, Luca Mülln, Florian Matthes
The increasing popularity of Large Language Models (LLMs) in recent years has changed the way users interact with and pose questions to AI-based conversational systems. An essentia…