Next GPT: Breakthrough in Multimodal Learning

Home AI News Next GPT: Breakthrough in Multimodal Learning

Next GPT: Breakthrough in Multimodal Learning

Introduction
What is Multi-Modal Learning?
Challenges in Multi-Modal Learning
Next GPT: A Breakthrough Model
Next GPT Architecture
Encoder Tier
Projection Layer
LLM Stage: Understanding and Reasoning
Vikuna Model
Modality Switching Instruction Tuning (MOSIT)
Training Next GPT
Inference with Next GPT
Conclusion

Introduction

Artificial Intelligence (AI) has made significant advancements in multi-modal learning, where AI models can process and learn from multiple modalities such as text, images, videos, and audio. However, most existing models in this field fall under the "any to one" regime, meaning they only output a single modality, typically text. This limitation highlights the need for models capable of processing and outputting any modality, which brings us to Next GPT.

What is Multi-Modal Learning?

Multi-modal learning refers to the field of AI where models are trained to learn and process data from multiple modalities, such as text, images, videos, and audio. This approach allows AI models to leverage the strengths and information from various modalities, leading to a more comprehensive understanding of the data.

Challenges in Multi-Modal Learning

While multi-modal learning shows promise, there are several challenges that need to be addressed. One of the main challenges is the "any to one" regime, where existing models can only output a single modality, typically text. This limitation restricts the potential of AI models to fully utilize and generate content in different modalities.

Next GPT: A Breakthrough Model

Next GPT is a groundbreaking model that explores the frontier of multi-modal learning by addressing the limitations of existing models. It is the first model to operate in the "any to any" regime, meaning it can take in any modality as input and output any modality.

Next GPT Architecture

The Next GPT model architecture can be broadly divided into three tiers: the encoder, the projection layer, and the LLM (Language Models) stage. The encoder is responsible for encoding the input, while the projection layer projects other modalities, such as audio or video, into text-like representations. At the heart of Next GPT is the LLM stage, which produces text tokens along with modality signals to guide the decoder in outputting content in the correct modality.

Encoder Tier

Several options are available for the encoder, but Next GPT utilizes Image Point from Meta. Image Point is chosen because it naturally handles multiple modalities, including text, audio, and depth. The outputs of Image Point are passed through projection layers to convert them into representations that Resemble LLM tokens.

Projection Layer

The projection layers in Next GPT are simple linear layers that convert the outputs of Image Point into representations that resemble LLM tokens as closely as possible. These projected representations are then passed to an LLM model called Vikuna, which is well-suited for multi-modal learning tasks.

LLM Stage: Understanding and Reasoning

The LLM stage in Next GPT is crucial in generating content in the desired modality. The output of Vikuna is not only text tokens but also multimodal instructions that guide the decoder in producing the correct content. These instructions, along with the LLM output tokens, are passed on to off-the-shelf decoder models such as Stable Diffusion for image synthesis, Xeroscope for video synthesis, and Audio LDM for audio synthesis.

Vikuna Model

Vikuna is an open-source LLM model that is used in Next GPT for multi-modal learning. It produces text tokens as well as multimodal instructions to dictate the output content. Although Vikuna is designed to work with Next GPT, it has never been trained with a model like Vikuna before. To Align Vikuna with these decoder models, Transformers with 31-32 million parameters are used.

Modality Switching Instruction Tuning (MOSIT)

One of the challenges in training Next GPT is the lack of existing datasets that provide instructions and outputs in different modalities. To overcome this challenge, the authors have proposed Modality Switching Instruction Tuning (MOSIT). They created a dataset called MOSIT by prompting GPT-4 with over 100 topics that require planning, reasoning, and Perception from the AI model to answer. The dataset consists of 5000 high-quality dialogues that went through human inspection and filtering.

Training Next GPT

Training Next GPT involves aligning the various components of the model through a process called instruction tuning. The training of the encoder projection layers is called Encode-Aside LLM Centric Alignment. It involves generating Captions using the LLM for a given input and aligning the projection layers based on these captions. Similarly, the decoder side projection layers are aligned using the output of Transformers and the corresponding encoder outputs.

Inference with Next GPT

Next GPT can handle various types of input, including text, audio, video, and images. For text input, the text tokens are directly passed to the LLM stage without going through projection layers. For non-text input, such as images with accompanying text, they are passed through the projection layers to transform the features and feed them to the LLM. The LLM then generates text tokens along with modality signal tokens to indicate the desired output modality.

Conclusion

Next GPT represents a significant advancement in multi-modal learning by operating in the "any to any" regime. It enables AI models to process and generate content in multiple modalities, including text, images, videos, and audio. With further research and development, we can expect even more sophisticated models that seamlessly interact with diverse modalities, bringing us closer to human-like understanding and interaction.

Highlights

Next GPT is a groundbreaking model in the field of multi-modal learning.
It operates in the "any to any" regime, processing and generating content in multiple modalities.
Next GPT utilizes Image Point, Vikuna, and Transformers to handle different modalities.
Modality Switching Instruction Tuning (MOSIT) addresses the challenge of dataset limitations for multi-modal learning.
Next GPT paves the way for more advanced AI models capable of interacting seamlessly with diverse modalities.

FAQ

Q: What is multi-modal learning?\ A: Multi-modal learning refers to the field of AI where models are trained to learn and process data from multiple modalities, such as text, images, videos, and audio.

Q: How does Next GPT differ from existing models in multi-modal learning?\ A: Next GPT is the first model that operates in the "any to any" regime, meaning it can take in any modality as input and output any modality, unlike existing models that often output only text.

Q: How does Next GPT align different modalities in the learning process?\ A: Next GPT aligns different modalities through the use of projection layers and Transformers, which convert the outputs of Image Point and Vikuna into compatible representations for synthesis by off-the-shelf decoder models.

Q: How was the MOSIT dataset created?\ A: The MOSIT dataset was created by prompting GPT-4 with over 100 topics that require planning, reasoning, and perception. The dataset consists of 5000 high-quality dialogues that went through human inspection and filtering.

Q: What are the potential applications of Next GPT and multi-modal learning?\ A: Next GPT and multi-modal learning have potential applications in various fields, such as content generation, video synthesis, and audio processing. They can enable AI models to interact seamlessly with diverse modalities, enhancing user experiences and enabling more sophisticated AI-driven solutions.

Q: Are there any limitations or challenges in training Next GPT?\ A: One challenge in training Next GPT is the lack of existing datasets that provide instructions and outputs in different modalities. The authors addressed this challenge by proposing Modality Switching Instruction Tuning (MOSIT) and creating the MOSIT dataset.

Resources: