ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

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.

If in a Crowdsourced Data Annotation Pipeline, a GPT-4

“…These situations are typical when computer science researchers develop datasets in specialized fields like biomedicine [34,36,39,40,46,67]. 8 In such instances, experts within the domain often are unable to label large datasets quickly, so out-of-domain experts-frequently CS graduate students-sometime need to label portions of the data to assess the crowd labels' quality. Second, the accuracy of the CS Expert's labels sets an estimated upper limit for non-expert performance, given their familiarity with the task and focused attention.…”