awesome-mllm-hallucination
github.com/showlab/awesome-mllm-hallucination ↗📖 A curated list of resources dedicated to hallucination of multimodal large language models (MLLM).
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Curated Resources
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Last Refreshed
Hallucination SurveyHallucination Evaluation & AnalysisHallucination Mitigation
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me hallucination evaluation & analysis resources from awesome-mllm-hallucination"
Installation instructions →What's inside
Hallucination Evaluation & Analysis
- A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models
- AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models
- ALOHa: A New Measure for Hallucination in Captioning Models
- A New Benchmark for Assessing Hallucination in Medical Large Language Models
- An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models
- An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Hallucination Mitigation
- A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning
- AGLA: Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
- Aligning Large Multimodal Models with Factually Augmented RLHF
- Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
- Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
- Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
Showing a sample of 198 resources. View the full list on GitHub →