6 citations · 14 across the 8 of their papers we have counts for
3 papers · 2 filters
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…
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…