6 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
Rylan Schaeffer, Noam Levi, Andreas Kirsch +4
Hoffman et al (2022)'s Chinchilla paper introduced the principle of compute-optimal scaling, laying a foundation for future scaling of language models. In the years since, however,…
Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs
Aryan Gulati, Brando Miranda, Eric Chen +5
Current mathematical reasoning benchmarks for large language models (LLMs) are approaching saturation, with some achieving > 90% accuracy, and are increasingly compromised by train…
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch +11
Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of…
Lean-ing on Quality: How High-Quality Data Beats Diverse Multilingual Data in AutoFormalization
Willy Chan, Michael Souliman, Jakob Nordhagen +3
Autoformalization, the process of transforming informal mathematical language into formal specifications and proofs remains a difficult task for state-of-the-art (large) language m…