Towards a Large Physics Benchmark
This provides a tool for the physics community to steer AI development, but it is incremental as it builds on existing benchmark concepts.
The authors introduced a benchmark framework for evaluating large language models in fundamental physics, featuring a scoring system based on correctness, difficulty, and surprise across multiple-choice, analytical, and open-ended tasks, with an initial dataset including a high-energy physics classification challenge.
We introduce a benchmark framework developed by and for the scientific community to evaluate, monitor and steer large language model development in fundamental physics. Building on philosophical concepts of scientific understanding and creativity, we develop a scoring system in which each question is scored by an expert for its correctness, difficulty, and surprise. The questions are of three forms: (i) multiple-choice questions for conceptual understanding, (ii) analytical problems requiring mathematical derivation, and (iii) openended tasks requiring complex problem solving. Our current dataset contains diverse set of examples, including a machine learning challenge to classify high-energy physics events, such as the four top quark signal. To ensure continued relevance, we propose a living benchmark, where physicists contribute questions, for instance alongside new publications. We invite contributions via: http://www.physicsbenchmarks.org/. We hope that this benchmark will enable a targeted AI development that can make a meaningful contribution to fundamental physics research.