A Survey of Hallucinations in Multimodal Large Language Models with Mitigation Strategies

Hallucinations in generative artificial intelligence and large language models: tests, datasets, detection and correction methods

This review paper provides a comprehensive overview of hallucinations in GAI and LLMs, and synthesizes a range of correction and mitigation techniques, from proactive measures during training to hybrid approaches that combine detection and intervention.

KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation

By unifying four hallucination dimensions with paired question design, KnowHal addresses an important gap in existing evaluation frameworks and enables a more comprehensive assessment of hallucinations in MLLMs.

Detecting and Mitigating Hallucinations in Large Language Models: A Comparative Study of Generative and Transformer-Based Approaches

The results suggest that no single architecture guarantees factual reliability, however, contextual grounding and verification mechanisms can significantly improve response quality and highlight the importance of combining language modelling capabilities with grounding strategies to support the development of more reliable AI systems.

Mitigating Hallucinations in Large Language Models via Retrieval Augmented Generation: A Systematic Review of n8n-Based Implementations

This study proposes a novel conceptual framework and taxonomy for hallucination mitigation in low-code AI environments, integrating retrieval, validation, conflict resolution, and workflow orchestration mechanisms to contribute to the development of more reliable, transparent, and scalable AI systems.

Survey of AI Hallucinations and Mitigation Survey of AI Hallucinations and Mitigation

UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space

Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence, so a fully black-box framework that models hallucination as a structured uncertainty pattern is proposed.