M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for
  Evaluating Foundation Models
Authors: name: Chuhan Li, name: Ziyao Shangguan, name: Yilun Zhao, name: Deyuan Li, name: Yixin Liu, name: Arman Cohan

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 workflows, which typically involve interpreting
non-textual data and gathering information across multiple documents. To
address this gap, we introduce M3SciQA, a multi-modal, multi-document
scientific question answering benchmark designed for a more comprehensive
evaluation of foundation models. M3SciQA consists of 1,452 expert-annotated
questions spanning 70 natural language processing paper clusters, where each
cluster represents a primary paper along with all its cited documents,
mirroring the workflow of comprehending a single paper by requiring multi-modal
and multi-document data. With M3SciQA, we conduct a comprehensive evaluation of
18 foundation models. Our results indicate that current foundation models still
significantly underperform compared to human experts in multi-modal information
retrieval and in reasoning across multiple scientific documents. Additionally,
we explore the implications of these findings for the future advancement of
applying foundation models in multi-modal scientific literature analysis.