The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech
Open accessAbstract
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.
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Cites
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549
e-SNLI: Natural language inference with natural language explanations
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., & Blunsom, P. (2018) · arXiv · doi:10.48550/arxiv.1812.01193 · Cited by 367 · cited 1×
“Moreover, it has been a challenging problem to provide the explainability of those models by using natural language explanations (NLEs) [4, 8].”
Background
Cited by
Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463
ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks
Gilardi, F., Alizadeh, M., & Kubli, M. (2023) · arXiv · doi:10.48550/arxiv.2303.15056 · Cited by 1,207
ChatGPT outperforms crowd workers for text-annotation tasks
Gilardi, F., Alizadeh, M., & Kubli, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305016120 · Cited by 1,197
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888
“As LLMs continue to improve, they show potential in replacing human annotators in many tasks [14, 17].”
BackgroundSummary of ChatGPT-Related research and perspective towards the future of large language models
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100017 · Cited by 855
Metadata
- Authors
- Huang, F., Kwak, H., & An, J.
- Venue
- Companion Proceedings of the ACM Web Conference 2023
- Publisher
- ACM
- Published
- 2023-04-30
- Pages
- 294-297
- OpenAlex
- W4327811957
- Cited by
- 224
- References
- 2
- FWCI
- 42.34
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Huang, F., Kwak, H., & An, J. (2023). Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech. Companion Proceedings of the ACM Web Conference 2023. https://doi.org/10.1145/3543873.3587368
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