ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
Submission Type: Regular Long Paper
Submission Track: Natural Language Generation
Submission Track 2: Semantics: Lexical, Sentence level, Document Level, Textual Inference, etc.
Keywords: natural language generation, paraphrase generation, crowdsourcing, large language models, intent classification, text diversity
TL;DR: ChatGPT can produce diverse paraphrases that lack named entity variations, while models trained on such data are robust on OOD data.
Abstract: 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 and Falcon-40B. We show that ChatGPT-created paraphrases are more diverse and lead to at least as robust models.
Submission Number: 1578
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