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20232026
most citedImproving Health Question Answering with Reliable and Time-Aware Evidence Retrieval

2 citations · 8 across the 17 of their papers we have counts for

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20 papers · 1 filter

cs.CL2026

Not All Fallbacks Are Failures: Understanding and Recovering from Fallbacks in Mobile Voice Assistants

Phillip Schneider, Alexandre Mercier, Joshua Oehms +2

Robust understanding of user input is a core requirement for voice assistants deployed in real-world environments. In practice, these systems encounter heterogeneous fallback situa…

cs.CL2025

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL20251 cited

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…