Summary of Dynamic Gradient Alignment For Online Data Mixing, by Simin Fan et al.
Dynamic Gradient Alignment for Online Data Mixing
by Simin Fan, David Grangier, Pierre Ablin
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 tackles the challenge of training large language models (LLMs) for specific downstream tasks using limited examples. The composition of training data mixtures has a direct impact on model performance, and traditional methods like reweighting, importance sampling, and gradient alignment have limitations. The authors focus on gradient alignment and propose Dynamic Gradient Alignment (DGA), an online algorithm that estimates the pre-training data mixture aligning with the task-specific model’s gradients. DGA offers minimal overhead compared to standard pre-training and outputs competitive models, eliminating the need for retraining. Experimental results show significant improvements over importance sampling in scenarios where the pre-training set is small or there is limited specialized data. This research demonstrates the effectiveness of gradient alignment methods in optimizing training data mixtures, particularly in data-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart language model that can understand and generate human-like text. But to make it really good at doing specific things, like answering questions or writing stories, you need to train it on the right kind of examples. The problem is, you might not have enough examples to work with. This paper figures out how to help the model learn from very few examples by adjusting what kinds of information it sees during training. They come up with a new way called Dynamic Gradient Alignment that can do this really efficiently and get good results. It’s like finding the right combination of ingredients for your favorite recipe, but instead of flour and sugar, you’re working with language and ideas. |
Keywords
» Artificial intelligence » Alignment » Language model
