Summary of M3sciqa: a Multi-modal Multi-document Scientific Qa Benchmark For Evaluating Foundation Models, by Chuhan Li et al.
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
by Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, Arman Cohan
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces M3SciQA, a benchmark designed to comprehensively evaluate foundation models on multi-modal, multi-document scientific question answering tasks. The existing benchmarks mainly focus on single-document text-only tasks, neglecting the complexity of research workflows that involve interpreting non-textual data and gathering information across multiple documents. M3SciQA consists of 1,452 expert-annotated questions spanning 70 NLP paper clusters, requiring foundation models to retrieve and reason across multiple documents, mirroring human workflow. The results show that current foundation models underperform compared to human experts in multi-modal information retrieval and reasoning. The findings have implications for the future advancement of applying foundation models in multi-modal scientific literature analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are not good at understanding complex research workflows that involve different types of data, such as images or audio, and gathering information from multiple documents. A new benchmark called M3SciQA is designed to test these models on their ability to answer questions based on this type of data. The benchmark consists of thousands of expert-annotated questions that cover a range of topics in natural language processing. Researchers used 18 different foundation models to test the benchmark, and found that they are not as good at answering questions as human experts. This has implications for how we use these models in the future. |
Keywords
» Artificial intelligence » Multi modal » Natural language processing » Nlp » Question answering
