6 papers
Explicit Evidence Grounding via Structured Inline Citation Generation
Anar Yeginbergen, Amelie Wührl, Anna Rogers +1
As AI systems become more widely adopted, the demand for factual and faithful generation grows. Properly attributing information through citations becomes, therefore, crucial. This…
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…
Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit
Aswathy Velutharambath, Amelie Wührl
Patients' expectations towards their treatment have a substantial effect on the treatments' success. While primarily studied in clinical settings, online patient platforms like med…
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…
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…
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…