Aligning Large Multi-Modal Model with Robust Instruction Tuning
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/.
649a894ee6a4622602515e2b
Fuxiao Liu
claimed_verified
2023-06-28T20:15:42.780Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64618b9496259bec21d44704/FCzN0p7Elh8MeUTuM-hya.jpeg
Fuxiao Liu
user
Fuxiao
649a894ee6a4622602515e2c
Kevin Lin
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2023-07-05T18:56:22.304Z
6298fd95b58e71e2ac9f3ad8
/avatars/7d34644d537bc5c17cf1e4ce4095355c.svg
Kevin Lin
user
kevinlin311tw
649a894ee6a4622602515e2d
Linjie Li
649a894ee6a4622602515e2e
Jianfeng Wang
claimed_verified
2023-06-28T20:15:39.830Z
641404e74e5305c14f269b3c
/avatars/0dbe862cde7e4bef25bedf860584d8c1.svg
Jianfeng Wang
user
amsword8
649a894ee6a4622602515e2f
Yaser Yacoob
649a894ee6a4622602515e30
Lijuan Wang
Aligning Large Multi-Modal Model with Robust Instruction Tuning
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/.
A new dataset and evaluation method are introduced to reduce hallucinations in large multi-modal models by incorporating both positive and negative instructions, improving model robustness and performance.
multi-modal models hallucination LRV-Instruction GPT4 visual instruction tuning GAVIE MiniGPT4 Existent Element Manipulation Nonexistent Element Manipulation