13 citations · 23 across the 23 of their papers we have counts for
12 papers · 1 filter
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…