MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models

• URL: http://arxiv.org/abs/2406.06046v1
• Date: Mon, 10 Jun 2024 06:27:42 GMT
• Title: MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models
• Authors: Zichun Yu, Spandan Das, Chenyan Xiong,
• Abstract summary: We introduce model-aware data selection with data influence models (MATES) We fine-tune a small data influence model to approximate oracle data preference signals collected by locally probing the pretraining model. Experiments on Pythia and the C4 dataset demonstrate that MATES significantly outperforms random data selection on extensive downstream tasks.
• Score: 16.654859430784825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretraining data selection has the potential to improve language model pretraining efficiency by utilizing higher-quality data from massive web data corpora. Current data selection methods, which rely on either hand-crafted rules or larger reference models, are conducted statically and do not capture the evolving data preferences during pretraining. In this paper, we introduce model-aware data selection with data influence models (MATES), where a data influence model continuously adapts to the evolving data preferences of the pretraining model and then selects the data most effective for the current pretraining progress. Specifically, we fine-tune a small data influence model to approximate oracle data preference signals collected by locally probing the pretraining model and to select data accordingly for the next pretraining stage. Experiments on Pythia and the C4 dataset demonstrate that MATES significantly outperforms random data selection on extensive downstream tasks in both zero- and few-shot settings. It doubles the gains achieved by recent data selection approaches that leverage larger reference models and reduces the total FLOPs required to reach certain performances by half. Further analysis validates the ever-changing data preferences of pretraining models and the effectiveness of our data influence models to capture them. Our code is open-sourced at https://github.com/cxcscmu/MATES.

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