toloka/mu-math
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Vitaliy Polshkov
cogwheelhead
AI & ML interests
Data-centric AI methods; Experiment design, statistical learning and causal inference approaches for LLM / agentic evaluations; "Proper old-school" RL and meta-learning techniques for LLM training
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ProcessBench: Identifying Process Errors in Mathematical Reasoning
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ProcessBench: Identifying Process Errors in Mathematical Reasoning
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Hey!
Me and my team recently released two benchmarks on university-level math: U-MATH (for University-MATH) and μ-MATH (for Meta U-MATH).
We're working a lot on complex reasoning for LLMs, and we were in particular interested in evaluating university-curricula math skills — in topics such as differential calculus and linear algebra — for their wide applicability and practicality.
We noticed that available benchmarks at the time were either at or below high-school level, or mainly leaning towards Olympiad-style problems, or synthetically generated from a set of templates / seeds.
We wanted focus on university curricula and we wanted "organic" variety, so we created our own benchmark using problems sourced from actual teaching materials used in top US universities — that is how U-MATH came to be.
We also, and that is my primary focus in particular, are very eager on studying and improving evaluations themselves, since the standard llm-as-a-judge approach is known to be noisy and biased, but that often remains unaccounted for. So we then created a U-MATH-derived benchmark to do "meta-evaluations" — i.e. evaluate the evaluators — which allows to quantify their error-rates, study their behaviors and biases, and so on.
I'm super excited to be sharing those publicly!
https://huggingface.co/datasets/toloka/u-math
https://huggingface.co/datasets/toloka/mu-math
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