Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech

By Fan Huang, Haewoon Kwak, Jisun An. 2023

348 citations recorded by the source corpus.

Recent studies have alarmed that many online hate speeches are implicit. With its subtle nature, the explainability of the detection of such hateful speech has been a challenging problem. In this work, we examine whether ChatGPT can be used for providing natural language explanations (NLEs) for implicit hateful speech detection. We design our prompt to elicit concise ChatGPT-generated NLEs and conduct user studies to evaluate their qualities by comparison with human-written NLEs. We discuss the potential and limitations of ChatGPT in the context of implicit hateful speech research.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

ChatGPT outperforms crowd workers for text-annotation tasks (2023, background): “While a few studies suggested that ChatGPT might perform text annotation tasks of the kinds we have described (Kuzman et al., 2023; Huang et al., 2023), to the best of our knowledge no systematic evaluation has been conducted yet.”
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena (2023, background): “As LLMs continue to improve, they show potential in replacing human annotators in many tasks [14, 17].”
Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023, methodology): “[21] used ChatGPT to detect implicit hate speech in tweets.”
Is ChatGPT a Good NLG Evaluator? A Preliminary Study (2023, background): “…obtained promising results on various natural language understanding (NLU) and natural language generation (NLG) downstream tasks (Ouyang et al., 2022; Koco ´ n et al., 2023; Qin et al., 2023; Huang et al., 2023; Yang et al., 2023; Rao et al., 2023; Bang et al., 2023; Zuccon and Koopman, 2023).”
Can Large Language Models Be an Alternative to Human Evaluations? (2023, methodology): “During the reviewing process, we found some works on arXiv (Wang et al., 2023; Huang et al., 2023; Gilardi et al., 2023) that explore a similar idea using different tasks.”
Learning to Compress Prompts with Gist Tokens (2023, methodology): “Recent work has found that ChatGPT can be used for text annotation and evaluation (Gilardi et al., 2023; Huang et al., 2023; Wang et al., 2023) with comparable performance to human annotation, and similar model-based evaluations have been conducted with recent LMs (Taori et al.”
Chain of Explanation: New Prompting Method to Generate Quality Natural Language Explanation for Implicit Hate Speech (2022, background): “Due to its societal importance, researchers have actively studied and proposed various models to detect hate speech [7, 14].”
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023, methodology): “Huang et al. [32] chose 12.5% of individuals in the potential hate dataset as study materials, induced ChatGPT to make classifications based on a prompt, and ChatGPT produced three classifications: unclear, yes, and no.”