Published on Sep 18
Authors:Yadong Lu,Chunyuan Li,Haotian Liu,Jianwei Yang,Jianfeng Gao,Yelong Shen
Abstract
Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open-source LMM are performed using models with 13B parameters or smaller. In this paper we present an empirical study of scaling LLaVA up to 33B and 65B/70B, and share our findings from our explorations in image resolution, data mixing and parameter-efficient training methods such as LoRA/QLoRA. These are evaluated by their impact on the multi-modal and language capabilities when completing real-world tasks in the wild. We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning. Additionally, the study highlights the importance of higher image resolutions and mixing multimodal-language data to improve LMM performance, and visual instruction tuning can sometimes improve LMM's pure language capability. We hope that this study makes state-of-the-art LMM research at a larger scale more accessible, thus helping establish stronger baselines for future research. Code and checkpoints will be made public.
Commentary
The paper "An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models" aims to bridge the gap in understanding the implications of scaling large multimodal models (LMM). These models are designed to process both text and images, making them immensely valuable for tasks that require multi-modal understanding.
Key Takeaways:
Scaling Beyond 13B: Previous research on open-source LMMs has primarily been centered around models with up to 13B parameters. This study takes it a notch higher by scaling LLaVA up to 33B and even 65B/70B, providing insights into the dynamics of even larger models.
Exploration Domains: The study focuses on aspects like image resolution, data mixing, and parameter-efficient training methods like LoRA/QLoRA, shedding light on their effects on model performance.
Consistent Benefits from Scaling: The research found that scaling the LMM size consistently enhances its performance. Furthermore, certain training techniques like LoRA/QLoRA can achieve comparable performance to full-model fine-tuning, but with better parameter efficiency.
Resolution & Data Mixing: The study underscores the importance of higher image resolutions and the need to mix multimodal-language data to realize better performance from LMMs.
Enhancing Language Capabilities: Interestingly, visual instruction tuning can sometimes even boost the LMM's pure language capabilities, further emphasizing the intertwined nature of multimodal learning.
Potential Real-World Impact:
Diverse Applications: With the capability to process both text and images, LMMs can be deployed in a vast array of applications, such as visual question answering, image captioning, content moderation, and more.
Performance Boost: Organizations and researchers can benefit from the enhanced performance by scaling LMMs, which can lead to more accurate results in real-world tasks.
Cost-effective Fine-tuning: Techniques like LoRA/QLoRA can enable researchers to achieve high performance without the overheads of full-model fine-tuning, resulting in cost and time savings.
Better Image Analysis: The emphasis on high-resolution image input could improve the quality of visual data processing in various domains, from medical imaging to satellite imagery analysis.
Promotion of Open Science: The authors' intention to make code and checkpoints public encourages the wider AI community to experiment, replicate, and potentially enhance the findings.
Challenges:
Computational Constraints: Scaling up to models as large as 65B/70B requires significant computational resources, which might not be accessible to many researchers and developers.
Transfer to Practical Scenarios: While the research provides solid baselines, real-world deployment in specific industries might require domain-specific adjustments.
Given the increasing emphasis on multimodal understanding in a plethora of applications, from e-commerce to healthcare:
I'd rate the real-world impact of this paper as a 9 out of 10.
The insights provided by the paper can significantly influence the design, training, and deployment of future multimodal systems, paving the way for more intuitive and efficient human-AI interactions across domains.
