Model Performance Begins with Data: Researchers from Ai2 Release DataDecide—A Benchmark Suite to Understand Pretraining Data Impact Across 30K LLM Checkpoints

The challenge of selecting data in LLM PRTRING

The development of large language models requires a large account investment, especially when experimenting with a pre -size alternative company. Wide comparison of data collections – by arranging billions of parameters and hundreds of billions of symbols – can consume hundreds of thousands of graphics processing hours per operation. Consequently, practitioners resort to widely smaller experiences as agents of great style behavior. However, these “experimental” studies are rarely published, as it produces a fragmented scene in which each laboratory is repeated similar tests on a small scale without common standards or methodologies. This ostrich impedes cloning, reduces collective visions, and obscures real exchanges between the development account and the final model performance.

Datadecide

To address these restrictions, AI2 AI2 Institute, in cooperation with the University of Washington and the University of Pennsylvania, is launched today Datadecide– A comprehensive set of experiences controlled by 25 distinguished groups and 14 sizes from 4 million to 1 billion teachers. Datadecide data groups include well -known sources such as Dolma, DCLM, ENSISERWEB, C4 and Fineweb, as well as the differences produced by detection in the field, consumed communications, quality nomination, and source mixing. Each model is trained on a preserved percentage to the distinctive stable symbol from 100 (100 symbols per teacher), which reflects the “excessive training” system that improves the efficiency of reasoning. In total, more than 1050 model and more than 30,000 checkpoints – everything is evaluated via ten clusters – for the public.

The artistic structure and pragmatic benefits

Datadecide organizes experiments along three axes:

• Data recipes: Each embodies the twenty -five of Pretering Corpora, each of which embodies different stimulating strategies (see Table 1 in the paper to obtain full recipe specifications).
• Model scale: Fourteen parameters’ formations (4 m – 1b), software derived via the OLMO model ladder to ensure hyperactivity, consistent with the standards. Each unparalleled scale includes two peers “stopping early”, while parameter models are characterized by three restarting seeds to measure the contrast.
• Evaluation suite: The OLMES standard for ten multi -options (for example, MMLU, Arc Easy/Challenge, HELlaswag, MBPP, Humaneval) provides a multi -faceted vision for language understanding, logical thinking, and code generation performance.

By launching each of the pre -data collections and opposite models, Datadecide enables researchers to:

• Reuse the new evaluation checkpoints without re -training.
• Experience with new prediction methods (for example, advanced scaling attacks, sneaking techniques).
• Investigation of standard sensitivity to training data and model scale.

The main results and quantitative visions

The systematic analysis of Datadecide results in four practical guidelines:

• The durability of the foundation line: Corpora arrangement is accurately ran by a small range (for example, 150 meters), a resolution of approximately 80 percent to predict the best data set in target 1 B – P -Parameter. On the other hand, eight extrapolation of the basic kidnapping does not exceed this simple induction, which confirms its cost effective.
• The sensitivity of the accredited account: The account budget required to make reliable decisions significantly vary depending on the task. Standards like MMLU and ARC are easy to predict with less than 0.01 percent of the targeted account, while HELlaswag and Socialiqa orders are more volatile to achieve similar resolution.
• Choose the agent measurement: Continuous probability measures – specifically the average possibility of the normal nature of the correct continuity (correct ProB) and the total probability (total Probps) – on the performance scale separate accuracy on small ranges. This is the most obvious in the tasks of code (MBPP, Humaneval), where the resolution of the decision jumps from near the lung to more than 80 percent with the correct Prob like the agent.
• Discipline and publishing considerations: The accuracy of the high decision is linked to the low contrast (noise) and wide performance sites across data sets. Agent measures that reduce noise or spread spreading and thus enhance the reliability of prediction directly.

The closing perspective

Datadecide converts the choice of pre -data from a dedicated art into a transparent science moved by data. By obtaining all the 25 models, 1050 models, 30,000+ checkpoints, embrace assessments and Jaithb, AI2 calls on society to reproduce results, expand assessments to new standards, and innovation in decision -making methods. As LLM continues to develop a galaxy account resources, Datadecide provides an initial framework to reduce lost experiences and increase insight to the maximum – giving the way towards artificial intelligence research more efficient, repetitive and cooperative.

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