Summary of Gpts Are Multilingual Annotators For Sequence Generation Tasks, by Juhwan Choi et al.
GPTs Are Multilingual Annotators for Sequence Generation Tasks
by Juhwan Choi, Eunju Lee, Kyohoon Jin, YoungBin Kim
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a solution to the time-consuming and expensive process of data annotation through crowdsourcing, particularly in low-resource languages. The proposed method utilizes large language models to autonomously annotate datasets, showing cost-efficiency and applicability to low-resource language annotation. Experimental results demonstrate the effectiveness of this approach, which can be applied to construct new datasets like image captioning datasets. To facilitate further research, the authors have made their source code open-source. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making it easier and cheaper to label data for machines to learn from. Right now, people are needed to do this labeling, but it takes a lot of time and money. The researchers found a way to use special language models to do the labeling automatically. This can be helpful especially when working with languages that don’t have as many speakers. They even created an image captioning dataset using their method and are sharing it for others to use. |
Keywords
» Artificial intelligence » Image captioning
