Benchmarks of MLLMs: Survey

A Survey on Benchmarks of Multimodal Large Language Models

¹Tencent, ²PKU, ²NUS, ²SEU, ²NJU

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📌 What is This Survey About?

Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives. This paper presents a comprehensive review of 200+ benchmarks and evaluations for MLLMs, focusing on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Finally, we discuss the limitations of the current evaluation methods for MLLMs and explore promising future directions. Our key argument is that evaluation should be regarded as a crucial discipline to better support the development of MLLMs.

Summary of 200 MLLM Benchmarks

Perception&Understanding

Comprehensive Evaluation

1. "Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want". Lin W, Wei X, An R, et al.. arXiv 2024. [Paper] [Github].
2. "CHEF: A COMPREHENSIVE EVALUATION FRAMEWORK FOR STANDARDIZED ASSESSMENT OF MULTIMODAL LARGE LANGUAGE MODELS". Shi Z, Wang Z, Fan H, et al. arXiv 2023. [paper] [Github].

Fine-grained Perception Image Understanding

Cognition&Reasoning

General Reasoning Knowledge-based Reasoning Intelligence&Cognition

Specific Domains

Text-rich VQA Decision-making Agents Diverse Cultures&Languages Other Applications

Key Capabilities

Conversation Abilities Hallucination Trustworthiness

Other Modalities

Videos Audio 3D Points Omni-modal