Summary of Understanding the Dataset Practitioners Behind Large Language Model Development, by Crystal Qian et al.
Understanding the Dataset Practitioners Behind Large Language Model Development
by Crystal Qian, Emily Reif, Minsuk Kahng
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 examines the role of dataset practitioners in large language model (LLM) development, focusing on Google’s technology company. The authors define this role through a retrospective analysis of teams contributing to LLM development, followed by semi-structured interviews with 10 practitioners. The study reveals that despite prioritizing data quality, there is no consensus on what data quality means or how it should be evaluated. As a result, practitioners either rely on intuition or write custom code to assess their data. The authors discuss potential reasons for this phenomenon and identify opportunities for alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at the people who prepare data for big language models like Google’s. The researchers talked to 10 of these “dataset practitioners” to understand what they do. They found that even though making sure the data is good is important, nobody agrees on what “good data” means or how to check it. So, some people just use their own ideas or write special code to make sure their data is okay. The authors think about why this might be happening and what can be done to fix it. |
Keywords
» Artificial intelligence » Alignment » Large language model
