8 papers · 1 filter
'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions
Nils Dycke, Marina Sakharova, Nico Daheim +1
Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitut…
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning
David Dinucu-Jianu, Jakub Macina, Nico Daheim +3
Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy which requires strategically withhold…
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Jakub Macina, Nico Daheim, Ido Hakimi +3
Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run eva…
A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs
Artem Shelmanov, Ekaterina Fadeeva, Akim Tsvigun +9
Large Language Models (LLMs) have the tendency to hallucinate, i.e., to sporadically generate false or fabricated information. This presents a major challenge, as hallucinations of…
Socratic Reasoning Improves Positive Text Rewriting
Anmol Goel, Nico Daheim, Christian Montag +1
Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language…
Token Weighting for Long-Range Language Modeling
Falko Helm, Nico Daheim, Iryna Gurevych
Many applications of large language models (LLMs) require long-context understanding, but models continue to struggle with such tasks. We hypothesize that conventional next-token p…