57 citations · 171 across the 13 of their papers we have counts for
5 papers · 1 filter
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
Jan Cegin, Branislav Pecher, Jakub Simko +3
The generative large language models (LLMs) are increasingly used for data augmentation tasks, where text samples are paraphrased (or generated anew) and then used for classifier f…
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?
Jan Cegin, Jakub Simko, Peter Brusilovsky
The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning…
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation
Branislav Pecher, Jan Cegin, Robert Belanec +3
While fine-tuning of pre-trained language models generally helps to overcome the lack of labelled training samples, it also displays model performance instability. This instability…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
Jan Cegin, Branislav Pecher, Jakub Simko +3
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to…