16 citations · 32 across the 14 of their papers we have counts for
16 papers
Prompt reinforcing for long-term planning of large language models
Hsien-Chin Lin, Benjamin Matthias Ruppik, Carel van Niekerk +6
Large language models (LLMs) have achieved remarkable success in a wide range of natural language processing tasks and can be adapted through prompting. However, they remain subopt…
Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
Carel van Niekerk, Renato Vukovic, Benjamin Matthias Ruppik +2
Large Language Models (LLMs) often produce plausible but poorly-calibrated answers, limiting their reliability on reasoning-intensive tasks. We present Reinforcement Learning from…
All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue
Shutong Feng, Hsien-chin Lin, Nurul Lubis +5
Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD systems must also maintain positive…
Text-to-SQL Task-oriented Dialogue Ontology Construction
Renato Vukovic, Carel van Niekerk, Michael Heck +5
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-orien…
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling
Michael Heck, Christian Geishauser, Nurul Lubis +6
Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains e…
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction
Benjamin Matthias Ruppik, Michael Heck, Carel van Niekerk +5
A common approach for sequence tagging tasks based on contextual word representations is to train a machine learning classifier directly on these embedding vectors. This approach h…