Dynamic Gradient Alignment for Online Data Mixing Article Swipe
Simin Fan
,
David Grangier
,
Pierre Ablin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.02498
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.02498
The composition of training data mixtures is critical for effectively training large language models (LLMs), as it directly impacts their performance on downstream tasks. Our goal is to identify an optimal data mixture to specialize an LLM for a specific task with access to only a few examples. Traditional approaches to this problem include ad-hoc reweighting methods, importance sampling, and gradient alignment techniques. This paper focuses on gradient alignment and introduces Dynamic Gradient Alignment (DGA), a scalable online gradient alignment algorithm. DGA dynamically estimates the pre-training data mixture on which the models' gradients align as well as possible with those of the model on the specific task. DGA is the first gradient alignment approach that incurs minimal overhead compared to standard pre-training and outputs a competitive model, eliminating the need for retraining the model. Experimentally, we demonstrate significant improvements over importance sampling in two key scenarios: (i) when the pre-training set is small and importance sampling overfits due to limited data; and (ii) when there is insufficient specialized data, trapping importance sampling on narrow pockets of data. Our findings underscore the effectiveness of gradient alignment methods in optimizing training data mixtures, particularly in data-constrained environments, and offer a practical solution for enhancing LLM performance on specific tasks with limited data availability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.02498
- https://arxiv.org/pdf/2410.02498
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403883987
All OpenAlex metadata
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https://openalex.org/W4403883987Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.02498Digital Object Identifier
- Title
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Dynamic Gradient Alignment for Online Data MixingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-03Full publication date if available
- Authors
-
Simin Fan, David Grangier, Pierre AblinList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.02498Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.02498Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.02498Direct OA link when available
- Concepts
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Mixing (physics), Computer science, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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