20 citations · 43 across the 25 of their papers we have counts for
26 papers
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
Mahdi Dhaini, Adam Dejl, Juraj Vladika +3
Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it re…
Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models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…
FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
Anum Afzal, Juraj Vladika, Florian Matthes
Large Language Models tend to struggle when dealing with specialized domains. While all aspects of evaluation hold importance, factuality is the most critical one. Similarly, relia…
MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature
Juraj Vladika, Florian Matthes
In the digital age, people often turn to the Internet in search of medical advice and recommendations. With the increasing volume of online content, it has become difficult to dist…
Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
Mahdi Dhaini, Juraj Vladika, Ege Erdogan +2
In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and…
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…
Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutes
Juraj Vladika, Stephen Meisenbacher, Florian Matthes
Lexical Substitution is the task of replacing a single word in a sentence with a similar one. This should ideally be one that is not necessarily only synonymous, but also fits well…
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey
Alexander Fichtl, Juraj Vladika, Georg Groh
Knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large-scale language models and domain-specific knowledge. KELMs can achieve hi…
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
Anum Afzal, Juraj Vladika, Gentrit Fazlija +2
Given the growing trend of many organizations integrating Retrieval Augmented Generation (RAG) into their operations, we assess RAG on domain-specific data and test state-of-the-ar…
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…
DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
Stephen Meisenbacher, Maulik Chevli, Juraj Vladika +1
The task of text privatization using Differential Privacy has recently taken the form of , in which an input text is obfuscated via the use of generative (…
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…
Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
Juraj Vladika, Stephen Meisenbacher, Martina Preis +2
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes.…
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…
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…
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…
Scientific Fact-Checking: A Survey of Resources and Approaches
Juraj Vladika, Florian Matthes
The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the va…
Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports
Juraj Vladika, Florian Matthes
With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendation…