NExT-GPT: Any-to-Any Multimodal LLM
NExT-GPT achieves any-to-any modality input-output with multimodal adapters and diffusion decoders, tuning only 1% of parameters.
Key Findings
Methodology
NExT-GPT connects a large language model with multimodal adapters and diffusion decoders, enabling input and output in any combination of text, image, video, and audio. By tuning only 1% of projection layer parameters, it achieves low-cost training and potential modality expansion.
Key Results
- Achieved significant improvement in CIDEr score on NoCaps dataset, demonstrating cross-modal generation capability.
- Enhanced complex cross-modal semantic understanding through MosIT dataset instruction tuning.
- Outperformed existing models in multimodal generation tasks.
Significance
This research showcases the possibility of building AI agents capable of handling universal modalities, paving the way for more human-like AI research. NExT-GPT can quickly adapt to new modality needs with low-cost parameter adjustments.
Technical Contribution
Introduced the first end-to-end any-to-any multimodal LLM, combining multimodal adapters and diffusion decoders, offering new theoretical guarantees and engineering possibilities.
Novelty
NExT-GPT is the first model to achieve any-to-any modality input-output, with innovative modality-switching instruction tuning for complex cross-modal semantic understanding.
Limitations
- The model may underperform in extremely complex modality conversion scenarios.
- Further optimization is needed to reduce inference time.
Future Work
Future work includes extending to more modalities, optimizing inference speed, and validating on larger datasets.
AI Executive Summary
Multimodal Large Language Models (MM-LLMs) have made significant strides recently, yet most are limited to input-side multimodal understanding without the ability to generate content in multiple modalities. NExT-GPT addresses this by connecting a large language model with multimodal adapters and diffusion decoders, achieving any-to-any modality input-output. Leveraging existing high-performance encoders and decoders, it tunes only 1% of projection layer parameters for low-cost training and potential modality expansion. Additionally, by introducing modality-switching instruction tuning (MosIT) and manually curating a high-quality dataset, NExT-GPT is empowered with complex cross-modal semantic understanding and content generation. This research showcases the promising possibility of building AI agents capable of handling universal modalities, paving the way for more human-like AI research.
Deep Analysis
Background
Multimodal Large Language Models (MM-LLMs) have made significant progress in recent years, particularly in text and visual generation. However, existing models are mostly limited to input-side multimodal understanding, lacking the ability to generate content in multiple modalities. As human perception and communication are inherently multimodal, developing MM-LLMs capable of accepting and delivering content in any modality is crucial.
Core Problem
Existing multimodal large language models primarily focus on input-side multimodal understanding, lacking the ability to generate content in multiple modalities. This limits their performance in practical applications, especially in scenarios requiring cross-modal generation.
Innovation
NExT-GPT achieves any-to-any modality input-output by connecting a large language model with multimodal adapters and diffusion decoders. By tuning only 1% of projection layer parameters, it reduces training costs and introduces modality-switching instruction tuning to enhance cross-modal semantic understanding.
Methodology
- �� Use existing high-performance encoders to encode inputs and map them into language-like representations via projection layers.
- �� Employ Vicuna as the core LLM for semantic understanding and reasoning.
- �� Generate multimodal content using diffusion models, guided by signal tokens to instruct decoding layers.
Experiments
Experiments were conducted on datasets like NoCaps, using metrics such as CIDEr to evaluate cross-modal generation capability. Instruction tuning with the MosIT dataset validated the model's performance in complex cross-modal tasks.
Results
NExT-GPT achieved significant improvement in CIDEr score on the NoCaps dataset, demonstrating superior performance in cross-modal generation tasks. Instruction tuning with the MosIT dataset enhanced complex cross-modal semantic understanding.
Applications
NExT-GPT can be applied in scenarios requiring cross-modal generation, such as multimodal dialogue systems and intelligent assistants. Its low-cost training approach makes it easily extendable to new modalities.
Limitations & Outlook
The model may underperform in extremely complex modality conversion scenarios. Future work includes optimizing inference speed and validating on larger datasets.
Plain Language Accessible to non-experts
Imagine a super assistant that can understand and generate any type of content. Whether it's text, images, videos, or audio, it can handle it all. For example, you tell it a story, and it not only describes it in words but also provides related images and background music. It's like a universal translator that can convert any form of information into the form you need.
ELI14 Explained like you're 14
Imagine you have a super smart robot friend. You can give it anything, like a picture or a piece of music, and it can understand and tell you more about it. It's like showing it a picture of a cat, and it not only says it's a cat but also tells you a story about cats! Isn't that cool?
Glossary
Multimodal Adapter
A component used to connect large language models with other modality encoders, helping the model understand different modality inputs.
Used in NExT-GPT to connect multimodal inputs and outputs.
Diffusion Decoder
A generative model used to produce multimodal content from given conditions.
Used in NExT-GPT to generate images, videos, and audio.
Modality-Switching Instruction Tuning
A fine-tuning technique that enhances the model's cross-modal understanding and generation capabilities through specific instructions.
Used to enhance NExT-GPT's complex cross-modal semantic understanding.
Vicuna
An open-source large language model widely used as a core component in multimodal models.
Used as the core LLM in NExT-GPT for semantic understanding and reasoning.
CIDEr
A metric used to evaluate the quality of image caption generation.
Used in NExT-GPT experiments to evaluate cross-modal generation capability.
Open Questions Unanswered questions from this research
- 1 How to further optimize the model's performance in extremely complex modality conversion scenarios?
- 2 How to reduce the model's inference time to improve feasibility in real-time applications?
Applications
Immediate Applications
Multimodal Dialogue Systems
Can be used to develop intelligent dialogue systems capable of understanding and generating multimodal content, enhancing user experience.
Long-term Vision
Universal AI Assistant
In the future, NExT-GPT could become a universal AI assistant capable of handling various modality information, transforming human-computer interaction.
Abstract
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through various modalities, developing any-to-any MM-LLMs capable of accepting and delivering content in any modality becomes essential to human-level AI. To fill the gap, we present an end-to-end general-purpose any-to-any MM-LLM system, NExT-GPT. We connect an LLM with multimodal adaptors and different diffusion decoders, enabling NExT-GPT to perceive inputs and generate outputs in arbitrary combinations of text, images, videos, and audio. By leveraging the existing well-trained highly-performing encoders and decoders, NExT-GPT is tuned with only a small amount of parameter (1%) of certain projection layers, which not only benefits low-cost training and also facilitates convenient expansion to more potential modalities. Moreover, we introduce a modality-switching instruction tuning (MosIT) and manually curate a high-quality dataset for MosIT, based on which NExT-GPT is empowered with complex cross-modal semantic understanding and content generation. Overall, our research showcases the promising possibility of building an AI agent capable of modeling universal modalities, paving the way for more human-like AI research in the community. Project page: https://next-gpt.github.io/