13 citations · 23 across the 23 of their papers we have counts for
24 papers
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…
Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models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…
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…
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…
CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding
Johannes Kirmayr, Lukas Stappen, Phillip Schneider +2
In today's assistant landscape, personalisation enhances interactions, fosters long-term relationships, and deepens engagement. However, many systems struggle with retaining user p…
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…
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
Tim Schopf, Alexander Blatzheim, Nektarios Machner +1
Scientific document classification is a critical task and often involves many classes. However, collecting human-labeled data for many classes is expensive and usually leads to lab…
Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs
Phillip Schneider, Florian Matthes
Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of sear…
Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs
Phillip Schneider, Nektarios Machner, Kristiina Jokinen +1
Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in…
NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing
Tim Schopf, Florian Matthes
Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, e…
Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components
Phillip Schneider, Wessel Poelman, Michael Rovatsos +1
Conversational search systems enable information retrieval via natural language interactions, with the goal of maximizing users' information gain over multiple dialogue turns. The…
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering
Juraj Vladika, Phillip Schneider, Florian Matthes
In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledg…
Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval
Juraj Vladika, Florian Matthes
In today's digital world, seeking answers to health questions on the Internet is a common practice. However, existing question answering (QA) systems often rely on using pre-select…
Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing
Phillip Schneider, Tim Schopf, Juraj Vladika +1
Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessant…
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification
Juraj Vladika, Florian Matthes
The increasing rate at which scientific knowledge is discovered and health claims shared online has highlighted the importance of developing efficient fact-checking systems for sci…
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
Phillip Schneider, Manuel Klettner, Elena Simperl +1
Generating natural language text from graph-structured data is essential for conversational information seeking. Semantic triples derived from knowledge graphs can serve as a valua…
Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs
Phillip Schneider, Manuel Klettner, Kristiina Jokinen +2
Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries f…
Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs
Juraj Vladika, Alexander Fichtl, Florian Matthes
Recent advances in natural language processing (NLP) owe their success to pre-training language models on large amounts of unstructured data. Still, there is an increasing effort t…
A Knowledge Graph Approach for Exploratory Search in Research Institutions
Tim Schopf, Nektarios Machner, Florian Matthes
Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to unde…
From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search
Phillip Schneider, Nils Rehtanz, Kristiina Jokinen +1
Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of…
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking
Juraj Vladika, Phillip Schneider, Florian Matthes
In the digital age, seeking health advice on the Internet has become a common practice. At the same time, determining the trustworthiness of online medical content is increasingly…
Challenges in Domain-Specific Abstractive Summarization and How to Overcome them
Anum Afzal, Juraj Vladika, Daniel Braun +1
Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as d…