PhysUniBenchmark has powerful support for multimodal problems, notably:
- Subject coverage: Comprehensive coverage of core areas of undergraduate physics such as classical mechanics (kinematics, dynamics), electromagnetism (electrostatic fields, circuits), optics (geometrical optics, fluctuation optics), fundamentals of thermodynamics and quantum mechanics
- diversity of topics::
- Word description questions: questions requiring conceptual understanding and formula derivation
- Image questions: include visual elements such as force analysis diagrams and optical path diagrams
- Compound questions: e.g. mechanics problems combining speed-time diagrams with textual descriptions
- data format::
- Structured data: using JSON templates to store questions and answers
- Image files: charts and diagrams in PNG/JPG format
- Mathematical expressions: formulas and symbols in LaTeX format
This multimodal design effectively tests the model's ability to correlate information across modalities, visualize and understand concepts, process mathematical notation, and synthesize reasoning. The dataset also supports user validation of format compatibility for customized questions via validate_data.py.
This answer comes from the articlePhysUniBenchmark: benchmarking tool for multimodal physics problemsThe
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