Detail výsledku
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
Pecher Branislav, Ing., Ph.D.
Šimko Jakub, doc. Ing., PhD., UPGM (FIT)
SRBA, I.
Bieliková Mária, prof. Ing., Ph.D., UPGM (FIT)
a další
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.
large language models, data augmentation, lexical diversity, text augmentation, crowdsourcing
@inproceedings{BUT193293,
author="ČEGIŇ, J. and PECHER, B. and ŠIMKO, J. and SRBA, I. and BIELIKOVÁ, M.",
title="Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation",
booktitle="Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
year="2024",
pages="13148--13171",
publisher="Association for Computational Linguistics",
address="Bangkok",
doi="10.18653/v1/2024.acl-long.710",
isbn="979-8-8917-6094-3",
url="https://aclanthology.org/2024.acl-long.710/"
}