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2023

Detecting and Preventing Hallucinations in Large Vision Language Models

Gunjal, Anisha, Yin, Jihan, Bas, Erhan

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Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in generalizing across a diverse set of multi-modal tasks, especially for Visual Question Answering (VQA).

However, generating detailed responses that are visually grounded is still a challenging task for these models.
We find that even the current state-of-the-art LVLMs (InstructBLIP) still contain a staggering 30 percent of the hallucinatory text in the form of non-existent objects, unfaithful descriptions, and inaccurate relationships.
To address this, we introduce M-HalDetect, a (M)ultimodal (Hal)lucination (Detect)ion Dataset that can be used to train and benchmark models for hallucination detection and prevention.

Detecting and Preventing Hallucinations in Large Vision Language Models · Around

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