Summary of Can Chatgpt Reproduce Human-generated Labels? a Study Of Social Computing Tasks, by Yiming Zhu et al.
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks
by Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, Gareth Tyson
First submitted to arxiv on: 20 Apr 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores whether large language models (LLMs) like ChatGPT can replicate human-generated label annotations in social computing tasks. To achieve this, the authors use ChatGPT to relabel five seminal datasets covering stance detection, sentiment analysis, hate speech, and bot detection. The results show that ChatGPT has the potential to handle these data annotation tasks, although there are challenges remaining. While overall accuracy is moderate at 0.609, performance varies substantially across individual labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT, a large language model, can do some things that humans used to do. Researchers want to know if it can label social computing data, like how people feel about certain topics or whether someone is being mean online. To find out, they had ChatGPT look at five sets of data and try to label them correctly. The results are promising, but not perfect. It’s like a smart assistant that can help with some tasks, but still needs human oversight. |
Keywords
» Artificial intelligence » Large language model
