Meet MathPile: A Diverse and High-Quality Math-Centric Corpus Comprising About 9.5 Billion Tokens

Superior conversational fashions like ChatGPT and Claude are inflicting important shifts in numerous merchandise and on a regular basis life. The important thing issue contributing to their success lies within the robustness of the foundational language mannequin. Reducing-edge foundational fashions are sometimes pre-trained utilizing intensive, numerous, and high-quality datasets encompassing numerous sources akin to Wikipedia, scientific papers, neighborhood boards, Github repositories, net pages, and extra. These foundational language fashions are anticipated to own well-rounded capabilities, together with language understanding, common sense reasoning, mathematical reasoning, language era, and extra.

A brand new research by Shanghai Jiao Tong College, Shanghai Synthetic Intelligence Laboratory, Nanjing College of Science and Expertise, and Generative AI Analysis Lab (GAIR) focuses on enhancing the mathematical reasoning capabilities inside foundational language fashions, which may probably improve purposes in schooling instruments, automated problem-solving, knowledge evaluation, code programming, and in the end improve consumer expertise. As a substitute of straight developing a mannequin, the main focus is making a high-quality and numerous pre-training dataset particularly tailor-made for the mathematics area, MATHPILE. 

This method stands out from earlier work in a number of elements. Prior open-source pre-training datasets have sometimes centered on basic domains (e.g., Pile, RedPajama, Dolma), multilingual elements, or programming languages (e.g., ROOTS and The Stack), missing a corpus particularly tailor-made for arithmetic. Though some datasets are designed for coaching math-specific language fashions (e.g., Minerva’s mathematical coaching dataset and OpenAI’s MathMix), these usually are not out there brazenly. 

Acknowledging this hole, this work goals to bridge this divide by creating an open-sourced mathematical corpus, democratizing entry to high-quality mathematical knowledge. This initiative allows researchers and builders to successfully and inclusively advance the capabilities of language fashions in mathematical reasoning. Concerning range, the corpus goes past net pages, integrating top-notch arithmetic textbooks, lecture notes, scientific papers from arXiv, and punctiliously chosen content material from authoritative platforms like StackExchange, ProofWiki, and Wikipedia. This positions the corpus as a richer and extra various mathematical useful resource for language fashions.

The researchers emphasize prime quality attributable to current research highlighting the adversarial results of low-quality and repetitive content material in pre-training datasets on mannequin coaching. For example, making a 1.3 billion-parameter code-focused mannequin was achieved by pre-training on rigorously curated net pages and artificial textbooks. It’s underscored that the standard of the corpus is extra essential than its amount. To attain this, the researchers undertook intensive preprocessing, cleansing, filtering, and deduplication efforts, dedicated to steady refinement and optimization to contribute distinctively to arithmetic.

The crew highlights that transparency and documentation are key elements. Totally documenting large-scale pre-training datasets is essential to figuring out biases or problematic content material. MATHPILE supplies complete documentation, together with traits, meant makes use of, and efforts to remove biases or undesirable content material to boost belief and value amongst practitioners.

This initiative goals to foster AI development in arithmetic by providing a specialised, high-quality, and numerous corpus tailor-made for the mathematical area whereas sustaining absolute transparency in knowledge for practitioners. The crew hopes that their work helps lay the inspiration for coaching extra highly effective mathematical problem-solving fashions sooner or later.

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