LLaVA-Phi: Limitations and What You Can Expect in the Future

Table of Links

Abstract and 1 Introduction

2. Related Work

3. LLaVA-Phi and 3.1. Training

3.2. Qualitative Results

4. Experiments

5. Conclusion, Limitation, and Future Works and References

5. Conclusion, Limitation, and Future Works

We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2. Our work demonstrates that such small vision-language models can perform effectively on standard benchmarks when combined with the LLaVA training methodology and a select dataset of high-quality data. The primary goal of our project is to aid the community in creating lightweight, multi-modal models capable of vision-language reasoning, optimized for operation on edge devices. This innovation paves the way for deploying multi-modal assistants in time-sensitive applications, such as robotics [35, 38].

Limitations. Given that Phi-2 utilizes the codegenmono [29] tokenizer and our model has not been specifically fine-tuned for following multilingual instructions, our LLaVA-Phi architecture is unable to process instructions in multiple languages, including Chinese.

Future Works. As language models have become significantly smaller in size compared to traditional vision-language models, they have become more accessible and affordable for the research community to explore fundamental concepts in vision-language integration. In future work, we plan to examine the impact of the size of the visual encoder and refine the training strategies for small language models, including approaches like direct preference optimization and RLHF, among other techniques. These efforts aim to further reduce model size while enhancing performance.

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