Summary of Hemm: Holistic Evaluation Of Multimodal Foundation Models, by Paul Pu Liang et al.
HEMM: Holistic Evaluation of Multimodal Foundation Models
by Paul Pu Liang, Akshay Goindani, Talha Chafekar, Leena Mathur, Haofei Yu, Ruslan Salakhutdinov, Louis-Philippe Morency
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 Holistic Evaluation of Multimodal Models (HEMM) to systematically evaluate the capabilities of multimodal foundation models across three dimensions: basic skills, information flow, and real-world use cases. HEMM assesses internal abilities required to solve problems, such as learning interactions across modalities, fine-grained alignment, multi-step reasoning, and handling external knowledge. Information flow studies how multimodal content changes during a task through querying, translation, editing, and fusion. The evaluation framework encompasses 30 tasks in various domains, including multimedia, affective computing, natural sciences, healthcare, and human-computer interaction. By analyzing performance trends regarding different modeling dimensions, the study identifies key dataset dimensions that pose challenges to today’s models. The findings highlight challenging multimodal interactions, use cases, and tasks requiring reasoning and external knowledge, as well as the benefits of data and model scale, and instruction tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to test how well computers can understand different types of information like images, videos, and text. This is important because it could help create more helpful computer systems that can work with lots of different kinds of data. The researchers created a special tool called HEMM that looks at three main things: what the computer can do on its own, how well it can understand relationships between different types of information, and how well it can apply what it knows to real-world problems. |
Keywords
* Artificial intelligence * Alignment * Instruction tuning * Translation
