Multimodal Model Hallucination Evaluation: A Deep Assessment Framework for Chinese Scenarios

multimodal-hallucination-evaluation is a project focused on multimodal model hallucination evaluation for Chinese scenarios, providing systematic assessment methods and datasets. The original author/maintainer is shuhan-123, the source platform is GitHub, the original title is the same as the project name, link: https://github.com/shuhan-123/multimodal-hallucination-evaluation, release time: 2026-06-16T10:14:50Z. This project aims to explore the nature of multimodal hallucination issues, propose an evaluation framework for Chinese scenarios, and is of great significance to Chinese AI applications.

Multimodal Large Language Models (MLLMs) have hallucination issues, which manifest in forms such as visual hallucination (incorrect object/attribute recognition), relational hallucination (incorrect description of element relationships), temporal hallucination (confusing the order of video events), and cultural hallucination (misunderstanding of cultural backgrounds). Chinese scenarios face unique challenges: 1. Images contain a lot of text requiring OCR + semantic understanding; 2. Dependence on specific cultural background knowledge (traditional festivals, internet slang, etc.); 3. Dialect and simplified/traditional Chinese differences leading to comprehension biases; 4. Scarcity of high-quality Chinese multimodal evaluation datasets.

The project builds a systematic evaluation framework: 1. Hierarchical evaluation system (from basic object recognition to complex relational reasoning); 2. Fine-grained annotated data (including object attributes, relationships, distractors, cultural information); 3. Adversarial test cases (semantically similar image pairs, misleading text-image pairs, cultural scenarios, ambiguous scenarios); 4. Automatic evaluation metrics (CHAIR measures the proportion of descriptions inconsistent with images, POPE tests object existence judgment, custom Chinese metrics such as text recognition accuracy). The evaluation workflow tools include data preprocessing, unified model interface (supports GPT-4V, Claude3, Gemini, Qwen-VL, etc.), batch evaluation, and visual reports (including hallucination rate, error case analysis).

Practical application value: 1. Provides data support for enterprise model selection; 2. Guides developers to improve models in a targeted manner; 3. Serves as a safety assessment tool for scenarios such as medical imaging and autonomous driving; 4. Becomes a standardized evaluation benchmark in academia. Comparison with existing work: Fills the gap in Chinese multimodal hallucination evaluation (most existing benchmarks are in English), focuses on cultural sensitivity (easily ignored by general benchmarks), and tool design emphasizes practicality (convenient for industrial application).

Summary: The project provides important evaluation infrastructure for Chinese multimodal AI, helps understand model limitations, and promotes the implementation of reliable applications. Future directions: 1. Expand video modality to evaluate temporal hallucination; 2. Support East Asian languages such as Japanese and Korean; 3. Build dynamic datasets to reflect the latest cultural phenomena; 4. Explore human-machine collaborative evaluation models.