Video #200: PALO: A Polyglot Large Multimodal Model for 5B People

Video #200: PALO: A Polyglot Large Multimodal Model for 5B People

Introduction

Hi, my name is Manish Gupta, and in this video, #200, I will discuss PALO, a polyglot large multimodal model designed to serve 5 billion people globally. PALO stands out due to its multilingual and multimodal capabilities.

What is PALO?

PALO is a large, multilingual, and multimodal model. It comes in three different sizes: 1.7 billion parameters, 7 billion parameters, and 13 billion parameters. Being multimodal means it can perform visual reasoning, and being multilingual means it can do so in ten languages—English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese—covering about 65% of the world's population.

Translation and Adaptation

One of the biggest challenges in training multilingual, multimodal models is the lack of diverse data. PALO addresses this by semi-automatically translating English multimodal instruction datasets into target languages. They use GPT-3.5 Turbo for translation but enhance the process with manual checks to address issues like grammatical nuances and punctuation errors.

Architecture and Performance

PALO employs the LLaVA architecture for its larger models (7 billion and 13 billion parameters) and Mobile VQ for its smaller model (1.7 billion parameters). Noteworthy about PALO is its use of a fine-tuned large language model and manual checks to handle translations effectively.

The model's architecture involves:

• Vision Encoder: Takes an image and converts it into vision embeddings.
• Projector: Projects vision embeddings into the text embedding space.
• Language Model: Processes text tokens and concatenated vision embeddings to generate responses.

Performance Analysis

PALO's performance is depicted through several key metrics:

• Accuracy: The PALO 13B model significantly outperforms the LLaVA 13B model, especially in low-resource languages.
• Multilingual Capabilities: PALO maintains decent performance across multiple languages, showing its strength compared to standard models like LLaVA and Mobile VQ.
• Dataset Utilization: By using manually reviewed translations and fine-tuning techniques, PALO achieves stronger performance, especially for languages with fewer resources.

Training Process

PALO uses a mix of automated and manual translation for its dataset. GPT-3.5 Turbo performs initial translations, followed by human review. The training involves:

1. Translating datasets using fine-tuned GPT-3.5 Turbo.
2. Utilizing approximately 1,000 human-reviewed conversations per language.
3. Fine-tuning the language model on these translations to ensure high accuracy in multiple languages.

Conclusion

PALO serves as a highly efficient multi-lingual, multi-modal model, offering versatility across different languages and modalities. With three sizes of checkpoints and support for ten languages, its comprehensive approach to data translation and model architecture sets a new precedent in this field.

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Keywords

• PALO
• Multilingual
• Multimodal
• Large Language Models
• Visual Reasoning
• Multilingual Translation
• LLaVA Architecture
• Mobile VQ

FAQ

1. What is PALO?

   • PALO is a polyglot large multimodal model designed to perform visual reasoning across ten different languages.

2. What sizes of models does PALO come in?

   • PALO is available in three sizes: 1.7 billion, 7 billion, and 13 billion parameters.

3. What languages does PALO support?

   • PALO supports English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese.

4. How does PALO handle multilingual data?

   • PALO uses GPT-3.5 Turbo for initial translations and supplements this with manual checks to refine accuracy.

5. What architecture does PALO use?

   • PALO employs the LLaVA architecture for its larger models and the Mobile VQ architecture for its smaller model.

6. What is the main advantage of PALO over other models?

   • PALO's significant advantage is its capability to perform well across multiple languages, particularly in low-resource languages, due to its comprehensive approach to data translation and model fine-tuning.

7. What kind of data does PALO use for training?

   • PALO is trained on a translated English dataset with substantial human review to address translational inconsistencies, making it highly accurate and versatile.