PALO, a multilingual Large Multimodal Model (LMM) developed by researchers from Mohamed bin Zayed University of AI, can answer questions in ten languages simultaneously. It bridges vision and language understanding across high- and low-resource languages, showcasing scalability and generalization capabilities, enhancing inclusivity and performance in vision-language tasks worldwide.
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Researchers from Mohamed bin Zayed University of AI Developed ‘PALO’: A Polyglot Large Multimodal Model for 5B People
Overview
Large Multimodal Models (LMMs), driven by AI advancements, revolutionize vision and language tasks but are mainly centered on English, neglecting non-English languages. This oversight excludes billions of speakers of languages like Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese. The lack of linguistic inclusivity underscores the need for broader representation in developing LMM to ensure effective communication across diverse global populations.
Practical Solutions and Value
Recent advancements in LMMs and LLMs have pushed the boundaries of natural language processing. Multilingual LLMs like BLOOM and PaLM address data skewness and cross-lingual performance challenges. Meanwhile, in LMMs, models like Qwen, mPLUG-Owl, and Ziya-Visual demonstrate bilingual capabilities, focusing on English and Chinese. These developments mark significant progress in multilingual understanding and processing of visual inputs. However, these LMMs remain limited to two languages.
The researchers from Mohamed bin Zayed University of AI and other institutes introduced PALO, a multilingual LMM capable of answering questions in ten languages simultaneously. They leverage a high-quality multilingual vision-language instruction dataset to train PALO, focusing on improving proficiency in low-resource languages while maintaining or enhancing performance in high-resource languages. PALO comprehends and generates content in ten major languages, bridging vision and language understanding across diverse global languages.
In evaluating PALO‘s multilingual capabilities, robust performance is observed across high-resource languages, with significant performance improvements in low-resource languages. PALO enhances inclusivity and performance in vision-language tasks across diverse global languages, catering to nearly two-thirds of the global population.
AI Solutions for Middle Managers
If you want to evolve your company with AI, stay competitive, and use AI for your advantage, consider leveraging PALO. It adeptly bridges vision and language understanding across ten languages, encompassing high-resource (e.g., English, Chinese) and low-resource (e.g., Arabic, Hindi) languages. By training on diverse, multilingual datasets and fine-tuning language translation tasks, PALO achieves significant performance improvements across various scales, showcasing its scalability and generalization capabilities.
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