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cs.CL2024
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
Chuhan Li, Ziyao Shangguan, Yilun Zhao +3
Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflow…
cs.CV2024★ 1 cited
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Ziyao Shangguan, Chuhan Li, Yuxuan Ding +4
Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understandi…