Despite the promising progress in multi-modal tasks, current large multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated image and human instructions. This paper addresses this issue by introducing the first large and diverse visual instruction tuning dataset, named Large-scale Robust Visual (LRV)-Instruction. Our dataset comprises 400k visual instructions generated by GPT4, covering 16 vision-and-language tasks with open-ended instructions and answers. Unlike existing studies that primarily focus on positive instruction samples, we design LRV-Instruction to include both positive and negative instructions for more robust visual instruction tuning. Our negative instructions are designed at three semantic levels: (i) Nonexistent Object Manipulation, (ii) Existent Object Manipulation and (iii) Knowledge Manipulation. To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts. GAVIE does not require human-annotated groundtruth answers and can adapt to diverse instruction formats. We conduct comprehensive experiments to investigate the hallucination of LMMs. Our results demonstrate existing LMMs exhibit significant hallucinations when presented with our negative instructions, particularly Existent Object and Knowledge Manipulation instructions. Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negative instances in the training data leads to a more robust model. Code and data are available at https://github.com/FuxiaoLiu/LRV-Instruction.

翻译：尽管多模态任务取得了显著进展，当前大型多模态模型（LMMs）仍易产生与图像及人类指令不一致的幻觉描述。本文首次提出大规模多样性视觉指令微调数据集——鲁棒大型视觉指令（LRV-Instruction）来解决该问题。该数据集包含由GPT4生成的40万条视觉指令，覆盖16项视觉-语言任务，采用开放式指令与答案。不同于现有研究主要聚焦正向指令样本，我们设计的LRV-Instruction同时包含正向和负向指令以实现更鲁棒的视觉指令微调。负向指令在三个语义层次设计：(i) 不存在对象操作、(ii) 存在对象操作和(iii) 知识操作。为高效评估LMMs产生的幻觉，我们提出GPT4辅助视觉指令评估（GAVIE）方法——一种类似人类专家评估视觉指令微调的稳定方案，该方法无需人工标注的基准答案且能适应多样化的指令格式。通过系统性实验探究LMMs幻觉现象，结果表明现有LMMs面对负向指令时存在显著幻觉，尤其在存在对象操作和知识操作指令场景。进一步，我们在LRV-Instruction上微调MiniGPT4和mPLUG-Owl成功缓解幻觉，并在多个公开数据集上取得优于现有方法的性能表现。此外，研究发现训练数据中正负样本的均衡配比有助于构建更鲁棒的模型。代码与数据详见https://github.com/FuxiaoLiu/LRV-Instruction 。