license: apache-2.0
datasets:
- allenai/datadecide
language:
- en
Because large language models are expensive to pretrain on different corpora, using smaller scale experiments to decide on data is crucial for reducing costs. Do datasets yielding better performance at small scale do the same at larger scale? And which predictive methods are most accurate? We conduct controlled pretraining experiments across 25 corpora with differing sources, deduplication, and filtering up to 100B tokens and model sizes up to 1B parameters. We release models, data, and evaluations in our DATADECIDE Suite as the most extensive openly available sweep of data decisions over scales and random seeds. We find that predictions based on experiments at single, rather than multiple, scales are most efficient. For example, 150M models trained with < 2% compute of 1B targets correctly decide 80% of comparisons and make better decisions than dividing the same compute budget between experiments at multiple scales and fitting scaling trends. While none of the 8 baseline scaling law methods we try exceed the compute-decision frontier established by single scale predictions, DATADECIDE can be used to measure improvements in future scaling prediction methods. We also identify that among 10 multiple choice benchmarks, MMLU and arc easy are highly predictable with as little as 4 orders of magnitude less compute, and that code evaluations MBPP and åçHumanEval can also be made predictable using continuous proxy metrics.
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