Summary of Scalable Data Ablation Approximations For Language Models Through Modular Training and Merging, by Clara Na and Ian Magnusson and Ananya Harsh Jha and Tom Sherborne and Emma Strubell and Jesse Dodge and Pradeep Dasigi
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
by Clara Na, Ian Magnusson, Ananya Harsh Jha, Tom Sherborne, Emma Strubell, Jesse Dodge, Pradeep Dasigi
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed method efficiently approximates data ablations for Large Language Models (LLMs) by training individual models on subsets of a training corpus and reusing them across evaluations of combinations of subsets. The method trains multiple models on distinct partitions of the large training corpus, allowing for inexpensive simulations of data ablations. This approach enables substantial improvements in amortized training efficiency, scaling linearly with respect to new data, and opening up new avenues for improving model performance through rigorous, incremental data assessment and mixing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are trained on specific data compositions that can greatly affect their downstream performance. However, finding the best data mixture is challenging because it requires training a model from scratch each time. A new method makes it possible to simulate different data mixtures without having to train a new model every time. This method trains multiple models on smaller parts of the overall training dataset and then uses these trained models to evaluate different data mixtures. This approach saves time and computational resources, making it easier to find the best data mixture for improved performance. |
