55 citations
- Heidelberg UniversityDE6 papers
- Center for Applied LinguisticsUS2 papers
- Institute of LinguisticsRU2 papers
- Amazon (Germany)DE1 paper
- Amherst CollegeUS1 paper
- Amsterdam University CollegeNL1 paper
- Bielefeld UniversityDE1 paper
- Computing CenterRU1 paper
- Friedrich Schiller University JenaDE1 paper
- Georgetown UniversityUS1 paper
- Heinz Nixdorf StiftungDE1 paper
- Hochschule BielefeldDE1 paper
11 papers
Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests
Manar Ali, Judith Sieker, Sina Zarrieß +1
In human conversation, both interlocutors play an active role in maintaining mutual understanding. When listeners are uncertain about what speakers mean, for example, they can requ…
Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training
Marc Brinner, Sina Zarrieß
We propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. Our approach results in a single model that simultaneously c…
Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them
Marc Brinner, Tarek Al Mustafa, Sina Zarrieß
We investigate the use of LLM-generated data for continual pretraining of encoder models in specialized domains with limited training data, using the scientific domain of invasion…
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems
Judith Sieker, Simeon Junker, Ronja Utescher +4
We examine how users perceive the limitations of an AI system when it encounters a task that it cannot perform perfectly and whether providing explanations alongside its answers ai…
Natural Language Processing RELIES on Linguistics
Juri Opitz, Shira Wein, Nathan Schneider
Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence.…
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation
Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler
Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples…