title: Aligning Large Multi-Modal Model with Robust Instruction Tuning
summary: 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/.
paper_authors._id: 649a894ee6a4622602515e2b
paper_authors.name: Fuxiao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-28T20:15:42.780Z
paper_authors._id: 64618b9496259bec21d44704
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64618b9496259bec21d44704/FCzN0p7Elh8MeUTuM-hya.jpeg
paper_authors.fullname: Fuxiao Liu
paper_authors.type: user
paper_authors.user: Fuxiao
paper_authors._id: 649a894ee6a4622602515e2c
paper_authors.name: Kevin Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-05T18:56:22.304Z
paper_authors._id: 6298fd95b58e71e2ac9f3ad8
paper_authors.avatarUrl: /avatars/7d34644d537bc5c17cf1e4ce4095355c.svg
paper_authors.fullname: Kevin Lin
paper_authors.type: user
paper_authors.user: kevinlin311tw
paper_authors._id: 649a894ee6a4622602515e2d
paper_authors.name: Linjie Li
paper_authors._id: 649a894ee6a4622602515e2e
paper_authors.name: Jianfeng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-28T20:15:39.830Z
paper_authors._id: 641404e74e5305c14f269b3c
paper_authors.avatarUrl: /avatars/0dbe862cde7e4bef25bedf860584d8c1.svg
paper_authors.fullname: Jianfeng Wang
paper_authors.type: user
paper_authors.user: amsword8
paper_authors._id: 649a894ee6a4622602515e2f
paper_authors.name: Yaser Yacoob
paper_authors._id: 649a894ee6a4622602515e30
paper_authors.name: Lijuan Wang
paper_title: Aligning Large Multi-Modal Model with Robust Instruction Tuning
paper_summary: 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/.
paper_ai_summary: 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.
paper_ai_keywords: multi-modal models hallucination LRV-Instruction GPT4 visual instruction tuning GAVIE MiniGPT4 Existent Element Manipulation Nonexistent Element Manipulation