57 citations · 165 across the 17 of their papers we have counts for
6 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…
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…
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
Branislav Pecher, Ivan Srba, Maria Bielikova
When solving NLP tasks with limited labelled data, researchers typically either use a general large language model without further update, or use a small number of labelled samples…
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices
Branislav Pecher, Ivan Srba, Maria Bielikova
While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-calle…
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
Branislav Pecher, Ivan Srba, Maria Bielikova +1
In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervi…
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…