Abstract-Level Summary

The research introduces \gradientRGBMMVU53,93,20310,10,80, an advanced benchmark designed for assessing foundation models in expert-level, multi-discipline video comprehension. Comprising 3,000 expert-annotated questions across 27 subjects in Science, Healthcare, Humanities & Social Sciences, and Engineering, this benchmark advances the evaluation of models in applying domain-specific knowledge and expert reasoning. Evaluation of 32 multimodal foundation models revealed that while models like o1 and Gemini 2.0 performed best, they still lag behind human expert capabilities. The study emphasizes the need for continued advancements in video-based expert reasoning.

Introduction Highlights

The study addresses a critical evaluation gap in foundation models' ability to perform expert-level reasoning in specialized-domain videos. Unlike existing benchmarks focused on text or static images, it emphasizes the need to evaluate models' capabilities with dynamic, information-rich videos, essential in specialized fields like healthcare and engineering. The research aims to fill this gap with a robust, multidisciplinary benchmark to test and enhance model proficiency in understanding complex video content.

Methodology

The \gradientRGBMMVU53,93,20310,10,80 benchmark was developed using a textbook-guided approach, ensuring wide coverage of domain knowledge. It involved 67 expert annotators who curated QA examples from scratch, focusing on both breadth of domain knowledge and depth of expert reasoning. The dataset encompasses 1,529 unique videos with strict quality controls. Evaluation was conducted on 32 multimodal models using Chain-of-Thought (CoT) reasoning for enhanced performance assessment.

Key Findings

• The o1 model performed best among all evaluated models but still did not reach human expert levels.
• Human performance in the open-book setting was 86.8% accuracy, surpassing that of the evaluated models.
• CoT reasoning generally improved model performance, with notable impact on model accuracy.
• Open-sourced models lagged behind proprietary models but showed significant development progress.

Implications and Contributions

This benchmark sets a new standard for evaluating multimodal models in video-based expert-level tasks, highlighting critical gaps and providing insights for future improvements. It serves as a tool for developing models with better capacity for incorporating domain-specific reasoning, ultimately contributing to advancements in AI's application in specialized fields.

Conclusion

The study underscores the expertise gaps in multimodal foundation models, even with the most advanced models. It emphasizes the importance of focusing on video-based expert reasoning as an ongoing challenge, suggesting the necessity for models designed explicitly from System-2 thinking and richer multimodal contextual analysis. Future research should aim at further refining such models and addressing current shortcomings.

Glossary

1. Foundation Models: Large-scale AI models capable of generalizing across diverse domains and tasks due to extensive training on large datasets.
2. Multimodal Foundation Models: Models that integrate and process information across multiple modalities, such as text, images, and videos.
3. Expert-Level Reasoning: The application of specialized, domain-specific knowledge to comprehend and interpret complex information.
4. Chain-of-Thought (CoT) Reasoning: A technique where models generate intermediate steps in the reasoning process to improve analytical accuracy.
5. System-2 Thinking: A form of reasoning that involves deliberate and logical thinking, as opposed to instinctive or heuristic-driven processes.
6. Creative Commons License: A public copyright license enabling free distribution and modification of content under set conditions.
7. Benchmark Dataset: A standard dataset used to assess and compare the performance of AI models on specific tasks.