2 citations · 2 across the 6 of their papers we have counts for
6 papers
Enhancing SLM via ChatGPT and Dataset Augmentation
Tom Pieper, Mohamad Ballout, Ulf Krumnack +2
This paper explores the enhancement of small language models through strategic dataset augmentation via ChatGPT-3.5-Turbo, in the domain of Natural Language Inference (NLI). By emp…
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Enhancing small language models for real-life application deployment is a significant challenge facing the research community. Due to the difficulties and costs of using large lang…
Learning Disentangled Audio Representations through Controlled Synthesis
Yusuf Brima, Ulf Krumnack, Simone Pika +1
This paper tackles the scarcity of benchmarking data in disentangled auditory representation learning. We introduce SynTone, a synthetic dataset with explicit ground truth explanat…
Show Me How It's Done: The Role of Explanations in Fine-Tuning Language Models
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Our research demonstrates the significant benefits of using fine-tuning with explanations to enhance the performance of language models. Unlike prompting, which maintains the model…
Learning Disentangled Speech Representations
Yusuf Brima, Ulf Krumnack, Simone Pika +1
Disentangled representation learning in speech processing has lagged behind other domains, largely due to the lack of datasets with annotated generative factors for robust evaluati…
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
Yusuf Brima, Ulf Krumnack, Simone Pika +1
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statist…