Summary of Benchmark Data Repositories For Better Benchmarking, by Rachel Longjohn et al.
Benchmark Data Repositories for Better Benchmarking
by Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Digital Libraries (cs.DL)
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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 Machine learning algorithms are typically evaluated using standard benchmark datasets. While research has established guidelines for data and benchmarking practices, less attention has been paid to the data repositories where these datasets are stored, documented, and shared. This paper analyzes the landscape of benchmark data repositories and their role in improving benchmarking. The analysis highlights issues with datasets (e.g., representational harms, construct validity) and evaluation methods (e.g., overemphasis on a few datasets and metrics, lack of reproducibility). To address these concerns, the paper discusses considerations for designing and using benchmark data repositories, focusing on improving machine learning benchmarking practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are looking at how they test machine learning algorithms. They’re not just testing the algorithms themselves, but also the way they store and share the datasets used to evaluate them. This is important because sometimes these datasets can be biased or flawed, which affects the results of the tests. The researchers want to make sure that the testing process is fair and reliable. |
Keywords
* Artificial intelligence * Attention * Machine learning
