evaluation-task · Source-linked discovery

MaCBench: Probing the limitations of multimodal language models for chemistry and materials research

MaCBench is a comprehensive benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks across three core aspects: data extraction, experimental understanding, and results interpretation. The dataset comprises over 1100 high-quality multimodal questions manually curated by chemistry and materials experts across 34 subsets.