OneLLM: One Framework to Align All Modalities with Language
OneLLM aligns eight modalities with language using a unified framework, enhancing multimodal understanding.
Key Findings
Methodology
OneLLM employs a unified multimodal encoder and a progressive multimodal alignment pipeline. Initially, an image projection module is trained to connect a vision encoder with LLM. Then, a Universal Projection Module (UPM) is constructed by mixing multiple image projection modules and dynamic routing. Finally, more modalities are progressively aligned using the UPM.
Key Results
- OneLLM excels in 25 diverse benchmarks, including multimodal captioning, QA, and reasoning tasks, outperforming many existing specialized models.
- In VQA tasks, OneLLM-7B outperforms ChatBridge-13B and AnyMAL-13B, even surpassing AnyMAL with 70B parameters.
- OneLLM also shows strong performance in video QA and captioning tasks, particularly in NextQA and How2QA.
Significance
The significance of OneLLM lies in its ability to address the architectural differences of modality-specific encoders in multimodal large language models, enabling expansion to more modalities. This breakthrough provides new possibilities for multimodal understanding and reasoning tasks, advancing the field of multimodal AI in academia and industry.
Technical Contribution
OneLLM's technical contributions include a unified multimodal encoder and a universal projection module that effectively map multimodal inputs to language models. This approach differs from existing modality-specific encoders, offering new engineering possibilities and theoretical guarantees.
Novelty
OneLLM is the first to integrate eight distinct modalities within a single model using a unified framework and progressive multimodal alignment pipeline, addressing the scalability issue of existing methods.
Limitations
- OneLLM may underperform with rare or novel modalities due to its training data focusing on common modalities.
- The computational cost is high, especially when handling large-scale datasets.
Future Work
Future directions include optimizing OneLLM's computational efficiency, exploring the integration of more modalities, and validating its performance in practical applications. The authors suggest further research on training and optimizing such models on larger datasets.
AI Executive Summary
Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capabilities. However, existing works heavily rely on modality-specific encoders, which usually differ in architecture and are limited to common modalities. OneLLM presents a unified framework that aligns eight modalities with language using a unified multimodal encoder and a progressive multimodal alignment pipeline. Initially, an image projection module is trained to connect a vision encoder with LLM. Then, a Universal Projection Module (UPM) is constructed by mixing multiple image projection modules and dynamic routing. Finally, more modalities are progressively aligned using the UPM. OneLLM is evaluated on 25 diverse benchmarks, including multimodal captioning, question answering, and reasoning tasks, where it delivers excellent performance. Code, data, model, and online demo are available at https://github.com/csuhan/OneLLM.
The key technology of OneLLM lies in its unified multimodal encoder and universal projection module, enabling the model to effectively map multimodal inputs to language models. Unlike existing methods, OneLLM's encoder and projection module are shared across all modalities. The modality-specific tokenizer consists of only one convolution layer, converting input signals into a sequence of tokens. Additionally, learnable modality tokens are added to enable modality switching and transform input tokens of diverse lengths into tokens of a fixed length.
Experimental results show that OneLLM excels in multimodal tasks, especially in VQA tasks, where OneLLM-7B outperforms ChatBridge-13B and AnyMAL-13B, even surpassing AnyMAL with 70B parameters. Although OneLLM is not specifically designed for vision tasks, its results demonstrate that OneLLM can also reach the leading level in vision specialized LLMs, narrowing the gap between MMLLMs and vision LLMs.
Deep Analysis
Background
Multimodal large language models (MLLMs) have recently gained widespread attention due to their powerful capabilities in multimodal understanding and reasoning tasks. Traditionally, these models rely on modality-specific encoders, which often differ in architecture and are limited to common modalities such as images, audio, and video. This approach's limitations lie in its difficulty to expand to more modalities, particularly those that are uncommon or novel. To overcome these challenges, researchers have begun exploring unified multimodal encoders and projection modules to enhance model scalability and generality.
Core Problem
Existing multimodal large language models rely on modality-specific encoders that differ in architecture, limiting model scalability. The core problem is how to build a unified and scalable encoder capable of handling a wide range of modalities. Solving this problem is crucial as it will significantly enhance the generality and applicability of multimodal models.
Innovation
OneLLM's core innovation lies in proposing a unified multimodal encoder and a Universal Projection Module (UPM). This method effectively maps multimodal inputs to language models by mixing multiple image projection modules and dynamic routing. Unlike existing methods, OneLLM's encoder and projection module are shared across all modalities, providing new engineering possibilities and theoretical guarantees.
Methodology
- �� Train an image projection module to connect a vision encoder with LLM.
- �� Construct a Universal Projection Module (UPM) by mixing multiple image projection modules and dynamic routing.
- �� Progressively align more modalities using the UPM.
- �� Add learnable modality tokens to enable modality switching and transform input tokens of diverse lengths into fixed-length tokens.
Experiments
The experimental design includes evaluating OneLLM's performance on 25 diverse benchmarks, covering multimodal captioning, QA, and reasoning tasks. Datasets used include LAION-400M, WebVid-2.5M, WavCaps, etc. Key hyperparameters include learning rate, batch size, and training epochs. Ablation studies assess the contribution of different components.
Results
OneLLM excels in multimodal tasks, particularly in VQA tasks, where OneLLM-7B outperforms ChatBridge-13B and AnyMAL-13B, even surpassing AnyMAL with 70B parameters. In video QA and captioning tasks, OneLLM shows strong performance, especially in NextQA and How2QA.
Applications
OneLLM can be directly applied to multimodal captioning, QA systems, and reasoning tasks. Its unified framework allows the model to easily expand to more modalities, with significant industrial application potential, especially in scenarios requiring the handling of diverse modality data.
Limitations & Outlook
OneLLM may underperform with rare or novel modalities due to its training data focusing on common modalities. Additionally, the computational cost is high, especially when handling large-scale datasets. Future research can focus on optimizing computational efficiency and expanding to more modalities.
Plain Language Accessible to non-experts
Imagine you're in a large supermarket with various products: food, clothing, electronics, etc. Each product has its shelf and label. OneLLM is like a super-smart cashier who can quickly recognize each product and categorize it correctly. Whether you bring an apple, a T-shirt, or a phone, it can quickly identify and process it. This ability makes it perform exceptionally well in handling multimodal data, just like this cashier can handle various products.
ELI14 Explained like you're 14
Hey there! Imagine you have a super-smart robot friend who can understand anything you say, whether you express it with pictures, sounds, or words. This robot is like an all-in-one translator who can translate anything you say into a language that others can understand. It's like a superhero who can travel between different worlds, bringing information from one place to another. Isn't that cool?
Glossary
Multimodal Large Language Model (MLLM)
A language model capable of processing multiple modalities of data, typically used for multimodal understanding and reasoning tasks.
In this paper, MLLM is used to align multiple modalities of data with language.
Unified Multimodal Encoder
An encoder capable of processing multiple modality inputs, aimed at enhancing model scalability and generality.
OneLLM uses a unified multimodal encoder to align eight modalities.
Universal Projection Module (UPM)
A module used to map multimodal inputs to the language model space, achieved by mixing multiple projection modules and dynamic routing.
UPM is used in OneLLM to align more modalities.
Dynamic Routing
A mechanism used to control the contribution of multiple projection experts, increasing model capacity.
In UPM, dynamic routing is used to integrate outputs from multiple experts.
Modality Token
Learnable tokens used to enable modality switching and transform input tokens of diverse lengths into fixed-length tokens.
In OneLLM, modality tokens are used to handle data from different modalities.
Open Questions Unanswered questions from this research
- 1 How to expand to more modalities without increasing computational cost?
- 2 How to improve model performance on rare modalities?
Applications
Immediate Applications
Multimodal QA System
OneLLM can be used to develop smarter QA systems capable of handling image, audio, and text inputs.
Long-term Vision
All-in-One Multimodal Assistant
In the future, OneLLM could become an all-in-one multimodal assistant, helping people handle complex data in various scenarios.
Abstract
Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However, existing works rely heavily on modality-specific encoders, which usually differ in architecture and are limited to common modalities. In this paper, we present OneLLM, an MLLM that aligns eight modalities to language using a unified framework. We achieve this through a unified multimodal encoder and a progressive multimodal alignment pipeline. In detail, we first train an image projection module to connect a vision encoder with LLM. Then, we build a universal projection module (UPM) by mixing multiple image projection modules and dynamic routing. Finally, we progressively align more modalities to LLM with the UPM. To fully leverage the potential of OneLLM in following instructions, we also curated a comprehensive multimodal instruction dataset, including 2M items from image, audio, video, point cloud, depth/normal map, IMU and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks, encompassing tasks such as multimodal captioning, question answering and reasoning, where it delivers excellent performance. Code, data, model and online demo are available at https://github.com/csuhan/OneLLM