Summary of Data Curation Via Joint Example Selection Further Accelerates Multimodal Learning, by Talfan Evans et al.
Data curation via joint example selection further accelerates multimodal learning
by Talfan Evans, Nikhil Parthasarathy, Hamza Merzic, Olivier J. Henaff
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an innovative approach to large-scale pretraining, which involves jointly selecting batches of data rather than individual examples. This is achieved by using multimodal contrastive objectives that reveal dependencies between data and provide criteria for measuring the joint learnability of a batch. The authors derive a simple algorithm for selecting these batches, which significantly accelerates training compared to individually prioritized data points. To reduce computational overhead, they leverage advances in model approximation. The resulting approach, called multimodal contrastive learning with joint example selection (JEST), outperforms state-of-the-art models while using up to 13 times fewer iterations and 10 times less computation. JEST’s performance is dependent on the ability to steer data selection towards well-curated datasets via pretrained reference models, introducing a new dimension for neural scaling laws. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that when training AI models, it’s better to pick groups of data together rather than one piece at a time. This helps the model learn more efficiently and quickly. The authors came up with a way to do this using special math problems that reveal how different pieces of data are related. They then developed an easy-to-use formula for picking these groupings, which speeds up training by a lot. Using their method, called JEST, they were able to make the model perform better while using less time and computer power. |
Keywords
* Artificial intelligence * Pretraining * Scaling laws
