3 citations · 3 across the 3 of their papers we have counts for
8 papers · 1 filter
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
Maren Pielka, Tobias Schneider, Jan Terheyden +1
We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By uti…
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules
Maren Pielka, Svetlana Schmidt, Rafet Sifa
We introduce a novel data generation method for contradiction detection, which leverages the generative power of large language models as well as linguistic rules. Our vision is to…
Informed Named Entity Recognition Decoding for Generative Language Models
Tobias Deußer, Lars Hillebrand, Christian Bauckhage +1
Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition…
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models
Lars Hillebrand, Armin Berger, Tobias Deußer +8
Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text pass…
Improving Natural Language Inference in Arabic using Transformer Models and Linguistically Informed Pre-Training
Mohammad Majd Saad Al Deen, Maren Pielka, Jörn Hees +2
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contr…
Word Sense Disambiguation as a Game of Neurosymbolic Darts
Tiansi Dong, Rafet Sifa
Word Sense Disambiguation (WSD) is one of the hardest tasks in natural language understanding and knowledge engineering. The glass ceiling of 80% F1 score is recently achieved thro…