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Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
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Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
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Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which either require large-scare computational resources or depend on the detection result of external models. However, there remains an under-explored field to utilize the LVLM itself to alleviate object hallucinations. In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects. Therefore, we propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. In specific, we devise logical consistency probing to raise questions with logical correlations, inquiring about attributes from objects and vice versa. Whether their responses can form a logical closed loop serves as an indicator of object hallucination. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs. Comprehensive experiments conducted on three benchmarks across four LVLMs have demonstrated significant improvements brought by our method, indicating its effectiveness and generality.
Forward citations
Cited by 10 Pith papers
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
Object hallucinations in MLLMs arise from spatial multi-head inconsistency and temporal visual-attention fade; AFIP corrects both training-free via cross-head enrichment and gated historical enhancement, with supporti...
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
Object hallucinations in MLLMs track multi-head spatial inconsistency and temporal visual-attention fade; AFIP corrects both via cross-head enrichment and gated historical reinjection, reducing CHAIR/POPE rates training-free.
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DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models
Monitoring attention entropy and image-output correlation during decoding, then applying targeted contrastive corrections, reduces hallucination in multimodal LLMs without retraining.
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
AFIP is a training-free attention-correction method that cuts object hallucination rates in multimodal LLMs by concentrating cross-head attention and restoring faded visual attention.
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ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models
ORCA is an agentic reasoning framework that enhances factual accuracy and adversarial robustness of pretrained LVLMs via an Observe-Reason-Critique-Act loop with small vision models, reporting accuracy gains of up to ...
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ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models
ORCA is an inference-time agentic framework that boosts LVLM accuracy on hallucination benchmarks by 3.64-40.67% and adds adversarial robustness via cross-model validation with small vision tools.
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Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling
VRA is a training-free agentic framework that orchestrates off-the-shelf LVLMs with a reasoning model via iterative verification and refinement, raising accuracy on remote sensing VQA from 52.8% to 78.8% and deliverin...
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OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination
OmniDPO extends direct preference optimization with audio-video alignment and modality-degradation preference pairs to reduce omni-modal hallucination.
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Hallucination of Multimodal Large Language Models: A Survey
The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.
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Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
Links MLLM hallucinations to attention distraction and introduces AFIP to correct it via cross-head enrichment and dynamic historical attention without retraining.
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