Aligning Large Multi-Modal Model with Robust Instruction Tuning
Authors: name: Fuxiao Liu, name: Kevin Lin, name: Linjie Li, name: Jianfeng Wang, name: Yaser Yacoob, name: Lijuan Wang

Despite the promising progress in multi-modal tasks, current large
multi-modal models (LMM) 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 consists of 120k 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 two semantic levels: (i) Nonexistent Element Manipulation and (ii) Existent
Element Manipulation. To efficiently measure the hallucination generated by
LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a novel
approach to evaluate visual instruction tuning without the need for
human-annotated groundtruth answers and can adapt to diverse instruction
formats. We conduct comprehensive experiments to investigate the hallucination
of LMMs. Our results demonstrate that existing LMMs exhibit significant
hallucination when presented with our negative instructions, particularly with
Existent Element Manipulation instructions. Moreover, by finetuning MiniGPT4 on
LRV-Instruction, we successfully mitigate hallucination while improving
performance on public datasets using less training data 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. Our project link is available at https://fuxiaoliu.github.io/LRV/.