30 citations · 44 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they…
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
Mohamad Ballout, Ulf Krumnack, Gunther Heidemann +1
Investigating deep learning language models has always been a significant research area due to the ``black box" nature of most advanced models. With the recent advancements in pre-…