Summary of A Survey on Evaluation Of Multimodal Large Language Models, by Jiaxing Huang and Jingyi Zhang
A Survey on Evaluation of Multimodal Large Language Models
by Jiaxing Huang, Jingyi Zhang
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a comprehensive review of Multimodal Large Language Models (MLLMs) evaluation methods. It positions LLMs as the “brain” and various modality encoders as sensory organs, enabling human-like capabilities and potentially achieving artificial general intelligence (AGI). The review covers existing MLLM evaluation tasks, categorized based on capabilities assessed, including multimodal recognition, perception, reasoning, trustworthiness, and domain-specific applications. It also summarizes benchmarks into general and specific ones, reviews evaluation steps and metrics, and emphasizes the critical role of evaluation in advancing the field. Key models mentioned include GPT-4V and Gemini. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to evaluate Multimodal Large Language Models (MLLMs) which are trying to mimic human perception and reasoning. It talks about different ways to test these models, including their ability to recognize things, understand what’s going on, make decisions, and be trustworthy. It also mentions specific areas where the models can be tested, like science and medicine. The paper is important because it helps people who are working with MLLMs know how to make them better. |
Keywords
» Artificial intelligence » Gemini » Gpt
