Visual Instruction Tuning
Abstract: Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.
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Synopsis
Overview
- Keywords: Visual Instruction Tuning, Multimodal Models, Instruction-Following, LLaVA, GPT-4
- Objective: Extend instruction tuning to the multimodal space by developing a visual instruction-following model.
- Hypothesis: Instruction tuning can enhance the performance of multimodal models in following visual and language instructions.
- Innovation: Introduction of a data generation pipeline using GPT-4 to create multimodal instruction-following datasets, leading to the development of LLaVA.
Background
Preliminary Theories:
- Instruction Tuning: A method in NLP where models are trained to follow human instructions, enhancing their generalization capabilities.
- Multimodal Learning: Integrates multiple forms of data (e.g., text and images) to improve understanding and task performance.
- Vision-Language Models: Models that process and understand both visual and textual information, crucial for tasks requiring multimodal reasoning.
- End-to-End Training: A training approach where all components of a model are trained simultaneously, allowing for better integration of features.
Prior Research:
- Flamingo (2022): Demonstrated strong performance in zero-shot task transfer in multimodal contexts, establishing a benchmark for future models.
- BLIP-2 (2023): Introduced a framework for language-image pre-training, enhancing the capabilities of vision-language models.
- LLaMA (2023): An open-source language model that serves as a foundation for developing multimodal models like LLaVA.
- OpenFlamingo (2023): Aimed to extend the capabilities of LLaMA to include image inputs, paving the way for multimodal instruction-following.
Methodology
Key Ideas:
- Data Generation Pipeline: Utilizes GPT-4 to convert image-text pairs into instruction-following formats, creating a diverse dataset for training.
- Model Architecture: Combines a visual encoder (CLIP) with a language model (LLaMA) to create a large multimodal model (LLaVA).
- Two-Stage Training: Involves pre-training for feature alignment followed by end-to-end fine-tuning to enhance instruction-following capabilities.
Experiments:
- Multimodal Chatbot: Developed to demonstrate LLaVA's ability to engage in conversations about images, showcasing its instruction-following capabilities.
- Science QA Benchmark: Evaluated LLaVA's performance on a multimodal reasoning dataset, achieving state-of-the-art accuracy.
Implications: The methodology allows for the creation of robust multimodal models that can effectively follow complex visual and language instructions, enhancing their applicability in real-world tasks.
Findings
Outcomes:
- LLaVA achieved a relative score of 85.1% compared to GPT-4 on a synthetic multimodal instruction-following dataset.
- Fine-tuning on the Science QA dataset resulted in a new state-of-the-art accuracy of 92.53%.
- The model demonstrated strong reasoning capabilities, comparable to multimodal GPT-4, even with a smaller training dataset.
Significance: This research bridges the gap between text-only instruction tuning and multimodal instruction-following, establishing a new paradigm for developing visual assistants.
Future Work:
- Expanding the dataset to include more diverse image-text pairs for better generalization.
- Integrating additional visual models to enhance LLaVA's capabilities and performance.
Potential Impact: Pursuing these avenues could lead to significant advancements in the development of general-purpose visual assistants, improving their utility across various applications in AI and human-computer interaction.