Result Details
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, modification or evaluation. For some of these tasks, models like ChatGPT can potentially substitute human workers. In this study, we investigate whether this is the case for the task of paraphrase generation for intent classification. We apply data collection methodology of an existing crowdsourcing study (similar scale, prompts and seed data) using ChatGPT. We show that ChatGPT-created paraphrases are more diverse and lead to at least as robust models.
natural language generation, paraphrase generation, crowdsourcing, large language models, intent classification, text diversity
@inproceedings{BUT187127,
author="Ján {Čegiň} and Jakub {Šimko}",
title="ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness",
booktitle="Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
year="2023",
pages="1889--1905",
publisher="Association for Computational Linguistics",
address="Singapur",
doi="10.18653/v1/2023.emnlp-main.117",
isbn="979-8-8917-6060-8",
url="https://aclanthology.org/2023.emnlp-main.117/"
}