Summary of Article: Annotator Reliability Through In-context Learning, by Sujan Dutta et al.
ARTICLE: Annotator Reliability Through In-Context Learning
by Sujan Dutta, Deepak Pandita, Tharindu Cyril Weerasooriya, Marcos Zampieri, Christopher M. Homan, Ashiqur R. KhudaBukhsh
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes an in-context learning (ICL) framework called ARTICLE to estimate annotation quality through self-consistency, aiming to increase diverse perspectives in annotation while ensuring consistency. The framework is evaluated on two offensive speech datasets using multiple large language models (LLMs) and compared with traditional methods. Results show that ARTICLE can robustly identify reliable annotators, improving data quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure the people who label data are good at their job. It’s hard to know if someone is just not very good or if they have a different opinion. The researchers created a new way called ARTICLE that uses how well people agree with themselves to figure out if they’re good at labeling. They tested it on two big datasets and found that it works really well. |
