3 citations · 3 across the 6 of their papers we have counts for
7 papers
Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection
Renato Vukovic, Hsien-chin Lin, Carel van Niekerk +5
Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the source-is a major challenge for both prompted and fine-tuned language mode…
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…
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…
Infusing Emotions into Task-oriented Dialogue Systems: Understanding, Management, and Generation
Shutong Feng, Hsien-chin Lin, Christian Geishauser +6
Emotions are indispensable in human communication, but are often overlooked in task-oriented dialogue (ToD) modelling, where the task success is the primary focus. While existing w…
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction
Carel van Niekerk, Christian Geishauser, Michael Heck +6
Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks. The qua…