Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
Reduce hallucination in multi-modal models using LRV-Instruction dataset and GAVIE method.
Key Findings
Methodology
This paper introduces a large-scale visual instruction tuning dataset named LRV-Instruction, comprising 400k visual instructions generated by GPT4, covering 16 vision-language tasks. The dataset includes negative instructions at three semantic levels: Nonexistent Object Manipulation, Existent Object Manipulation, and Knowledge Manipulation. To evaluate hallucinations in multi-modal models, a GPT4-Assisted Visual Instruction Evaluation method (GAVIE) is proposed.
Key Results
- Result 1: Existing multi-modal models exhibit significant hallucinations when tested with negative instructions, especially under Existent Object and Knowledge Manipulation.
- Result 2: Fine-tuning MiniGPT4 and mPLUG-Owl on LRV-Instruction significantly reduces hallucinations and outperforms existing methods on several public datasets.
- Result 3: A balanced ratio of positive and negative instances in training data enhances model robustness.
Significance
This research provides an effective solution to the hallucination problem in multi-modal models by introducing a diverse visual instruction dataset and a new evaluation method. It not only enhances model robustness but also offers new research directions for future multi-modal tasks.
Technical Contribution
The technical contributions include the introduction of the first large-scale, diverse visual instruction dataset LRV-Instruction and the GAVIE method, which evaluates without human-annotated answers. These innovations offer new insights and tools for instruction tuning in multi-modal models.
Novelty
This is the first systematic introduction of negative instructions to reduce hallucinations in multi-modal models. Compared to existing studies, the diversity of the LRV-Instruction dataset and the flexibility of the GAVIE method are its core innovations.
Limitations
- Limitation 1: The GAVIE method relies on GPT4's accuracy, which may be unstable in complex scenarios.
- Limitation 2: There may be inaccuracies in task naming during dataset generation.
Future Work
Future research can explore more types of negative instructions and validate the effectiveness of LRV-Instruction and GAVIE on larger-scale multi-modal models.
AI Executive Summary
Multi-modal models often generate inconsistent descriptions with images, known as hallucinations. This issue not only affects model accuracy but can also lead to user misunderstanding. To address this, the paper introduces a large-scale visual instruction tuning dataset called LRV-Instruction, containing 400k visual instructions generated by GPT4, covering 16 vision-language tasks. Unlike previous studies, LRV-Instruction includes both positive and negative instructions designed at three semantic levels: Nonexistent Object Manipulation, Existent Object Manipulation, and Knowledge Manipulation. To evaluate hallucinations in multi-modal models, the researchers propose a GPT4-Assisted Visual Instruction Evaluation method (GAVIE). Experimental results show that existing multi-modal models exhibit significant hallucinations when tested with negative instructions, especially under Existent Object and Knowledge Manipulation. However, fine-tuning MiniGPT4 and mPLUG-Owl on LRV-Instruction significantly reduces hallucinations and outperforms existing methods on several public datasets. Additionally, the study finds that a balanced ratio of positive and negative instances in training data is crucial for enhancing model robustness. This research not only provides an effective solution to the hallucination problem in multi-modal models but also offers new directions and tools for future multi-modal task research.
Deep Analysis
Background
Recent advances in multi-modal models have shown significant progress in vision and language tasks. However, these models often suffer from hallucinations, where generated descriptions are inconsistent with image content. This issue not only affects model accuracy but can also lead to user misunderstanding. Existing studies primarily focus on positive instruction samples, overlooking the impact of negative instructions.
Core Problem
The hallucination problem in multi-modal models manifests as descriptions that are inconsistent with image content. This issue not only affects model accuracy but can also lead to user misunderstanding and incorrect decisions. Solving this problem is crucial for improving the usability and reliability of multi-modal models.
Innovation
The core innovation of this paper lies in the introduction of a large-scale, diverse visual instruction dataset LRV-Instruction, and the design of negative instructions at three semantic levels: Nonexistent Object Manipulation, Existent Object Manipulation, and Knowledge Manipulation. Additionally, a GPT4-Assisted Visual Instruction Evaluation method (GAVIE) is proposed.
Methodology
- �� LRV-Instruction dataset: Comprising 400k visual instructions generated by GPT4, covering 16 vision-language tasks.
- �� Negative instruction design: Includes Nonexistent Object Manipulation, Existent Object Manipulation, and Knowledge Manipulation.
- �� GAVIE evaluation method: Evaluates instruction-following performance and visual hallucination without human-annotated answers.
Experiments
Experiments were conducted on multiple public datasets, using LRV-Instruction to fine-tune MiniGPT4 and mPLUG-Owl. Evaluation metrics included model accuracy and robustness on positive and negative instructions. Results showed that fine-tuned models outperformed existing methods across various tasks.
Results
Results indicate that existing multi-modal models exhibit significant hallucinations when tested with negative instructions, especially under Existent Object and Knowledge Manipulation. Fine-tuned models on LRV-Instruction outperform existing methods on several public datasets.
Applications
Applications include multi-modal dialogue systems, image caption generation, and visual question answering. By reducing hallucinations, the usability and reliability of models are significantly enhanced.
Limitations & Outlook
Despite the effectiveness of LRV-Instruction and GAVIE in reducing hallucinations, they rely on GPT4's accuracy, which may be unstable in complex scenarios. Additionally, there may be inaccuracies in task naming during dataset generation.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a book about elephants. You ask the librarian, but they give you a book about lions instead. This is like the hallucination problem in multi-modal models: the descriptions generated by the model don't match the image content. To avoid this, researchers designed a new dataset to teach models how to better understand and describe image content. It's like the librarian learning more about animals to find the right book for you.
ELI14 Explained like you're 14
Hey there! Have you ever asked a friend a question and got a totally off answer? That's like the hallucination problem in multi-modal models. To make these models smarter, scientists designed a super big dataset to teach them how to better understand and describe pictures. Imagine giving your friend an encyclopedia so they can answer questions more accurately next time!
Glossary
Multi-Modal Model
A machine learning model capable of processing multiple data types, such as images and text.
Used for vision and language tasks.
Hallucination Problem
A phenomenon where the model generates descriptions inconsistent with actual data.
Occurs when multi-modal models generate inaccurate descriptions.
Instruction Tuning
The process of fine-tuning a model with specific instruction datasets to improve its performance.
Used to enhance the accuracy of multi-modal models.
GPT4
A large language model developed by OpenAI, known for its powerful text generation capabilities.
Used to generate the LRV-Instruction dataset.
GAVIE
GPT4-Assisted Visual Instruction Evaluation method for assessing instruction-following performance.
Used to evaluate hallucination in multi-modal models.
Open Questions Unanswered questions from this research
- 1 How to validate the effectiveness of LRV-Instruction and GAVIE on larger-scale multi-modal models?
- 2 Can more types of negative instructions be introduced to further reduce hallucinations?
Applications
Immediate Applications
Multi-Modal Dialogue Systems
By reducing hallucinations, improve the accuracy and user experience of dialogue systems.
Long-term Vision
Intelligent Visual Assistants
In the future, intelligent visual assistants could more accurately understand and describe the user's surroundings.
Abstract
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.