Summary of When Babies Teach Babies: Can Student Knowledge Sharing Outperform Teacher-guided Distillation on Small Datasets?, by Srikrishna Iyer
When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?
by Srikrishna Iyer
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper presents a novel approach to data-efficient language model pretraining, aiming to push the boundaries of BabyLM challenge submissions. The method builds upon deep mutual learning and introduces a student model search for diverse initialization. By formulating weighted mutual learning as a bi-level optimization problem, the authors address the limitation of treating students equally. This is achieved through online distillation in the inner loop and optimizing weights for better knowledge distillation from diverse students in the outer loop. The dynamic weighting strategy eliminates the need for a teacher model, reducing computational requirements. Evaluations show that teacher-less methods can match or surpass teacher-supervised approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to train language models using less data. It uses an approach called “deep mutual learning” and introduces a search method to find diverse starting points for the training process. By weighing the importance of different students, the authors make sure that each student is treated fairly and gets the right amount of attention. This helps the model learn more efficiently without needing a teacher model. The results show that this approach can be just as good or even better than traditional methods. |
Keywords
» Artificial intelligence » Attention » Distillation » Knowledge distillation » Language model » Optimization » Pretraining » Student model » Supervised » Teacher model
