6 citations · 14 across the 8 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…
Explaining Code Examples in Introductory Programming Courses: LLM vs Humans
Arun-Balajiee Lekshmi-Narayanan, Priti Oli, Jeevan Chapagain +4
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approach…
Human-AI Co-Creation of Worked Examples for Programming Classes
Mohammad Hassany, Peter Brusilovsky, Jiaze Ke +2
Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among t…
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…