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SUMMARY:Datasets for Math: From AIMO Competitions to Math Copilots for Research
DESCRIPTION:  \nNew Technologies in Mathematics Seminar \nSpeaker: Simon Frieder\, Oxford \nTitle: Datasets for Math: From AIMO Competitions to Math Copilots for Research \nAbstract: This talk begins with a brief exposition of the AI Mathematical Olympiad (AIMO) on Kaggle\, now in its second iteration\, outlining datasets and models available to contestants. Taking a broader perspective\, I then examine 1) the overarching issues the current datasets suffer from—such as binary evaluation or constrained sets of use cases— and 2) the trajectory they set for competition-style mathematical problem-solving\, which is different from mathematical research practice. I argue for a fundamental shift in dataset structure and composition\, both for training and evaluation\, and introduce the idea of mapping mathematical workflows to data\, a key example underscoring the need for this shift. I touch upon new thinking LLMs and their role in redefining LLM math evaluation\, highlighting their implications for dataset design. Finally\, I propose general improvements to the current state of mathematical datasets\, including mathematical adaptations of dataset documentation (e.g.\, datasheets). \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_22625/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
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