Effects of diversity incentives on sample diversity and downstream model
performance in LLM-based text augmentation
- URL: http://arxiv.org/abs/2401.06643v2
- Date: Thu, 15 Feb 2024 11:14:10 GMT
- Title: Effects of diversity incentives on sample diversity and downstream model
performance in LLM-based text augmentation
- Authors: Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova,
Peter Brusilovsky
- Abstract summary: We investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions.
We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.
- Score: 6.647958966528349
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 fine-tune downstream models. However, more
research is needed to assess how different prompts, seed data selection
strategies, filtering methods, or model settings affect the quality of
paraphrased data (and downstream models). In this study, we investigate three
text diversity incentive methods well established in crowdsourcing: taboo
words, hints by previous outlier solutions, and chaining on previous outlier
solutions. Using these incentive methods as part of instructions to LLMs
augmenting text datasets, we measure their effects on generated texts lexical
diversity and downstream model performance. We compare the effects over 5
different LLMs, 6 datasets and 2 downstream models. We show that diversity is
most increased by taboo words, but downstream model performance is highest with
hints.
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