Mitigating Object Hallucination via Data Augmented Contrastive Tuning

• URL: http://arxiv.org/abs/2405.18654v1
• Date: Tue, 28 May 2024 23:36:00 GMT
• Title: Mitigating Object Hallucination via Data Augmented Contrastive Tuning
• Authors: Pritam Sarkar, Sayna Ebrahimi, Ali Etemad, Ahmad Beirami, Sercan Ö. Arık, Tomas Pfister,
• Abstract summary: Multimodal Large Language Models (MLLMs) tend to hallucinate factually inaccurate information. We introduce a contrastive tuning method that can be applied to a pretrained off-the-shelf MLLM for mitigating hallucinations.
• Score: 52.43197107069751
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite their remarkable progress, Multimodal Large Language Models (MLLMs) tend to hallucinate factually inaccurate information. In this work, we address object hallucinations in MLLMs, where information is offered about an object that is not present in the model input. We introduce a contrastive tuning method that can be applied to a pretrained off-the-shelf MLLM for mitigating hallucinations while preserving its general vision-language capabilities. For a given factual token, we create a hallucinated token through generative data augmentation by selectively altering the ground-truth information. The proposed contrastive tuning is applied at the token level to improve the relative likelihood of the factual token compared to the hallucinated one. Our thorough evaluation confirms the effectiveness of contrastive tuning in mitigating hallucination. Moreover, the proposed contrastive tuning is simple, fast, and requires minimal training with no additional overhead at inference.

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