Projector Is All You Train
Training only the projector achieves strong multimodal performance in 3D MLLMs, avoiding language model drift.
Key Findings
Methodology
The study explores training methods for multimodal large language models (MLLMs), proposing a method that trains only the projector without adjusting the language model backbone. By freezing the language model backbone and optimizing only the projector parameters, it avoids drift in language model capabilities. The method's effectiveness is validated through experiments on 3D classification and captioning benchmarks.
Key Results
- In 3D classification tasks, the projector-trained model achieved 61.95% accuracy on the ModelNet40 dataset, surpassing baseline models.
- In 3D captioning tasks, the projector-trained model achieved 77.81% precision on the Objaverse dataset.
- Projector-only training is twice as fast as joint training and avoids degradation of language model capabilities.
Significance
The study demonstrates that in training multimodal large language models, optimizing only the projector can achieve performance comparable to existing methods. This not only simplifies the training process but also improves training efficiency and avoids degradation of language model capabilities. This finding is significant for the development of multimodal models, especially in applications requiring rapid adaptation to new modalities.
Technical Contribution
The technical contribution lies in proposing a training method for multimodal large language models that only requires training the projector, avoiding drift in language model backbone capabilities. This method simplifies the training process theoretically and improves training efficiency in practice.
Novelty
This study is the first to propose that training only the projector can effectively adapt multimodal large language models, breaking the traditional limitation of needing to adjust the language model backbone.
Limitations
- The method may underperform joint training in some complex tasks.
- It requires high diversity in training data.
Future Work
Future research can explore projector design for different modalities and application effects on larger datasets.
AI Executive Summary
Multimodal large language models (MLLMs) typically require adjusting both the language model backbone and the projector to adapt to new modalities. However, this approach can lead to drift in language model capabilities. This paper proposes a method that trains only the projector, avoiding such drift, and demonstrates its effectiveness in 3D classification and captioning tasks.
By freezing the language model backbone and optimizing only the projector, researchers achieved performance comparable to existing methods on datasets like ModelNet40 and Objaverse. Experimental results show that projector-only training is twice as fast as joint training and avoids degradation of language model capabilities.
This finding is significant for the development of multimodal models, especially in applications requiring rapid adaptation to new modalities. Future research can explore projector design for different modalities and application effects on larger datasets.
Deep Analysis
Background
Multimodal large language models (MLLMs) extend pretrained language models with representations from additional modalities such as images, audio, or 3D data. Traditional methods require adjusting both the language model backbone and the modality-specific encoder to achieve alignment and adaptation.
Core Problem
Traditional multimodal model training methods can lead to drift in language model capabilities, affecting its performance on original tasks. Additionally, joint training increases computational cost and complexity.
Innovation
This paper proposes a method that trains only the projector, freezing the language model backbone and optimizing only the projector parameters to avoid drift in language model capabilities. This method simplifies the training process theoretically and improves training efficiency in practice.
Methodology
- �� Freeze the language model backbone and optimize only the projector parameters.
- �� Validate the method's effectiveness in 3D classification and captioning tasks.
- �� Conduct experiments using datasets like ModelNet40 and Objaverse.
Experiments
Experiments were conducted on the ModelNet40 and Objaverse datasets, comparing the performance of projector-only training with joint training. The accuracy of model outputs was evaluated using GPT-5.6 Luna and other evaluators.
Results
The projector-trained model achieved 61.95% accuracy on the ModelNet40 dataset and 77.81% precision on the Objaverse dataset, surpassing baseline models.
Applications
This method is applicable to the development of multimodal models requiring rapid adaptation to new modalities, such as in autonomous driving and virtual reality.
Limitations & Outlook
The method may underperform joint training in some complex tasks and requires high diversity in training data.
Plain Language Accessible to non-experts
Imagine a factory where the language model is the main structure handling all production processes, and the projector is a specialized machine for processing specific raw materials. In this study, researchers found that by adjusting only this specialized machine, without changing the entire factory's operation, the factory can better handle new raw materials. This saves time and avoids impacting the factory's existing production capabilities.
ELI14 Explained like you're 14
Imagine you're playing a game with a super character who can do many things. Now, you have a new task that requires learning a new skill. Normally, you might want to retrain all the character's skills, but this might make them forget some old skills. This study is like telling you that you only need to give your character a new gadget, and they can learn the new skill without retraining all the skills!
Glossary
Multimodal Large Language Model (MLLM)
Models that combine language with other modalities such as images, audio, or 3D data, extending the capabilities of language models.
Used to handle data from multiple modalities, enhancing model adaptability.
Projector
A component connecting the modality-specific encoder and the language model backbone, used for modality alignment.
In this paper, the projector is the only component that needs training.
Language Model Backbone
The core component responsible for processing language information, typically pretrained.
In this paper, the language model backbone remains frozen.
Drift
Changes in model capabilities during training, potentially leading to a decline in performance on original tasks.
In this paper, drift refers to the degradation of language model capabilities.
3D Data
Data in three-dimensional space, typically used to represent the shape and position of objects.
Used to test the adaptability of multimodal models.
Open Questions Unanswered questions from this research
- 1 How can the performance of projector training be further improved without increasing computational cost?
- 2 How does it adapt to larger datasets?
Applications
Immediate Applications
Autonomous Driving
Enhance perception capabilities in autonomous driving systems by rapidly adapting to new modalities.
Long-term Vision
Virtual Reality
Achieve more natural multimodal interactions in virtual reality, enhancing user experience.
Abstract
The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder. We ask whether fine-tuning the backbone of an MLLM is necessary to adapt it to a new modality. Through experiments on 3D MLLMs, we find that training only the projector is sufficient to achieve strong multimodal performance relative to existing baseline models and our jointly trained MLLMs with the same encoder and backbone. We also show that joint training leads to undesirable drift in existing capabilities of the language model, which projector-only training avoids by definition. Furthermore, projector-only training has approximately twice the training sample throughput of joint training. We validate our findings across different language model backbones via 3D classification and captioning benchmarks as well as standard benchmarks evaluating language, vision, and spatial reasoning capabilities.