7 citations · 7 across the 8 of their papers we have counts for
10 papers
A Human-Centric Framework for Data Attribution in Large Language Models
Amelie Wührl, Mattes Ruckdeschel, Kyle Lo +1
In the current Large Language Model (LLM) ecosystem, creators have little agency over how their data is used, and LLM users may find themselves unknowingly plagiarizing existing so…
Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
Amelie Wührl, Roman Klinger
In fact-checking, structure and phrasing of claims critically influence a model's ability to predict verdicts accurately. Social media content in particular rarely serves as optima…
Which Demographics do LLMs Default to During Annotation?
Johannes Schäfer, Aidan Combs, Christopher Bagdon +9
Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message a…
How Entangled is Factuality and Deception in German?
Aswathy Velutharambath, Amelie Wührl, Roman Klinger
The statement "The earth is flat" is factually inaccurate, but if someone truly believes and argues in its favor, it is not deceptive. Research on deception detection and fact chec…
Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
Aswathy Velutharambath, Amelie Wührl, Roman Klinger
If a person firmly believes in a non-factual statement, such as "The Earth is flat", and argues in its favor, there is no inherent intention to deceive. As the argumentation stems…
Understanding Fine-grained Distortions in Reports of Scientific Findings
Amelie Wührl, Dustin Wright, Roman Klinger +1
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasin…
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification
Amelie Wührl, Yarik Menchaca Resendiz, Lara Grimminger +1
Biomedical claim verification fails if no evidence can be discovered. In these cases, the fact-checking verdict remains unknown and the claim is unverifiable. To improve upon this,…
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
Amelie Wührl, Roman Klinger
False medical information on social media poses harm to people's health. While the need for biomedical fact-checking has been recognized in recent years, user-generated medical con…
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Isabelle Mohr, Amelie Wührl, Roman Klinger
Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose…
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Amelie Wührl, Roman Klinger
Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experience…