Summary of Dsdm: Model-aware Dataset Selection with Datamodels, by Logan Engstrom et al.
DsDm: Model-Aware Dataset Selection with Datamodels
by Logan Engstrom, Axel Feldmann, Aleksander Madry
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 models require high-quality training data to perform well, right? Wrong! Research shows that filtering data for “good” examples doesn’t always improve model behavior. In fact, choosing data similar to what’s considered “high quality” might even decrease performance compared to randomly selecting data. This paper investigates why this is the case and provides insights into how to better select training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trained on massive datasets, but have you ever wondered if filtering out bad data really makes a difference? Surprisingly, researchers found that choosing “good” data doesn’t always improve performance. In some cases, it might even make things worse! This study tries to figure out why this happens and what we can do about it. |
Keywords
* Artificial intelligence * Machine learning
