Survey of Multimodal Large Language Model Evaluation Benchmarks: A Systematic Review of Current Assessment Methods and Challenges

The open-source project Evaluation-Multimodal-LLMs-Survey maintained by swordlidev systematically organizes evaluation benchmarks for Multimodal Large Language Models (MLLMs), covering assessment methods, datasets, and metrics. This project provides a comprehensive reference for researchers and developers, helping them address evaluation challenges amid the rapid development of MLLMs. The project source is GitHub (link: https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey), released on May 26, 2026.

With the rise of vision-language models like GPT-4V and Gemini, MLLMs have become an active direction in the AI field. However, accurately and comprehensively evaluating their capabilities is a major challenge. This open-source project systematically organizes evaluation benchmarks, providing valuable reference resources for the industry against the backdrop of fast model iterations and emerging new benchmarks.

MLLMs are extended from traditional LLMs and can process text and visual information simultaneously. Their typical architecture includes:

1. Visual Encoder: Such as CLIP's ViT and EVA-CLIP, which convert visual content into feature vectors;
2. Projection Layer/Adapter: Connects visual and language modalities, mapping features to the language embedding space;
3. Language Model Backbone: Based on Transformer (e.g., LLaMA, Vicuna), processes input and generates output;
4. Training Strategy: Pre-training (large-scale image-text pair alignment) + instruction tuning (enhances instruction-following ability).

Evaluation benchmarks are divided into four categories:

• Visual Understanding Capability: Image classification, object detection, VQA, image captioning, visual reasoning;
• Cross-modal Alignment: Image-text retrieval, image-text matching, fine-grained alignment;
• Multimodal Reasoning: Mathematical reasoning, scientific reasoning, common-sense reasoning, logical reasoning;
• Specific Domains: Document understanding, medical image analysis, autonomous driving scenarios, robot vision.

Mainstream evaluation benchmarks include:

• Comprehensive: MME (perception + cognition), MMBench (standardized framework), SEED-Bench (20,000 multiple-choice questions), MM-Vet (GPT-4-assisted evaluation);
• Specialized Capability: TextVQA (image text understanding), ScienceQA (scientific reasoning), MathVista (mathematical charts), ChartQA (chart understanding);
• Hallucination Detection: POPE, HallusionBench, MMHal-Bench.

Challenges in evaluation:

1. Evaluation Metrics: Traditional accuracy is insufficient; need semantic similarity (BERTScore), human evaluation, GPT-4 assistance, and multi-dimensional assessment;
2. Data Contamination: Training data may include evaluation data; need dynamic evaluation, adversarial testing, and private test sets;
3. Blurred Capability Boundaries: Distinguishing between perception vs. cognition, memory vs. reasoning, single-modal vs. multi-modal;
4. Fairness and Bias: Issues of language (English-dominated), cultural, and domain biases.

Project value for different groups:

• Researchers: Quickly understand the overall landscape of the field, identify gaps, and select benchmarks to validate methods;
• Developers: Evaluate self-developed models, select scenario-adapted benchmarks, and guide productization decisions;
• Decision-Makers: Understand technical maturity, assess model applicability, and guide investment and strategy.

This survey project provides important knowledge organization resources for the MLLM field, promoting the healthy development of the industry. Future trends include:

• Integration of more modalities (audio, video, tactile, etc.);
• Real-time interactive evaluation (multi-turn dialogue, video stream understanding);
• Safety and alignment evaluation (content filtering, privacy protection);
• Interpretability evaluation (attention visualization, reasoning chain tracing).