Summary of Llms in the Loop: Leveraging Large Language Model Annotations For Active Learning in Low-resource Languages, by Nataliia Kholodna et al.
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
by Nataliia Kholodna, Sahib Julka, Mohammad Khodadadi, Muhammed Nurullah Gumus, Michael Granitzer
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 addresses the significant challenges faced by low-resource languages in AI development due to limited linguistic resources and expertise for data labeling. The authors propose leveraging large language models (LLMs) in an active learning loop for data annotation, which minimizes the amount of queried data required and achieves near-state-of-the-art performance. Empirical evaluations demonstrate estimated potential cost savings of at least 42.45 times compared to human annotation. This approach has promising potential to substantially reduce both monetary and computational costs associated with automation in low-resource settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low-resource languages face big challenges when it comes to using artificial intelligence (AI). One problem is that there isn’t enough data or expertise for labeling this data, making it hard to develop AI tools. The authors of this paper suggest a new way to use large language models (LLMs) to help with data annotation. This approach uses less data and achieves good results. In fact, it could save around 42 times more money than using human annotators. This could make AI more accessible to people who speak these languages. |
Keywords
* Artificial intelligence * Active learning * Data labeling
