Humans have began interacting with the world through the 2 best pillars of language and vision. That is all due to super good capabilities of the recently popularized Large Language Models (LLMs). LLMs have taken the world by storm with their significantly increasing performance. LLMs like GPT-3, T5, PaLM, etc., have began imitating humans by learning to read, summarize and generate textual data. 

Researchers in the sector of Artificial Intelligence have been developing a general-purpose assistant that may effectively follow multimodal vision-and-language instructions aligned with human intent to finish real-world tasks easily. For this, language-augmented foundation vision models in open-world visual understanding are being developed to perform tasks comparable to classification, detection, segmentation, captioning, visual generation, and editing. With the discharge of GPT-4 by OpenAI, the transformer model behind the famous chatbot, ChatGPT, and its multimodal capabilities of it have proved to be addition to the list of LLMs.

In a recent research paper, the authors have presented the primary try to use GPT-4 to generate multimodal language-image instruction-following data. The team has introduced LLaVA, a Large Language and Vision Assistant, an end-to-end trained large multimodal model connecting a vision encoder and Vicuna for general-purpose visual and language understanding. Vicuna is an open-source chatbot with 13B parameters which has been trained by fine-tuning LLaMA on user-shared conversations. 

LLaVa is an try to extend instruction tuning to the multimodal space. The essential objective is to enable users to have their real-time tasks accomplished with the assistance of a visible assistant that may effectively follow multimodal vision-and-language instructions aligned with human intent. The numerous contributions made by the team are as follows – 

1. Multimodal instruction-following data – The team has presented an information reformation perspective and pipeline to convert image-text pairs into the instruction-following format with the assistance of the GPT-4 model.
2. Large multimodal models – The team has developed a big multimodal model by connecting the open-set visual encoder of CLIP with the language decoder LLaMA and fine-tuning them end-to-end on the generated instructional vision-language data.
3. The empirical study tries to validate the effectiveness of user-generated data for LMM instruction tuning. It even suggests practical suggestions for constructing a general-purpose instruction-following visual agent. 
4. SOTA performance has been achieved with the assistance of GPT-4 on the Science QA multimodal reasoning dataset.
5. Open-Source nature – The project is open source, and the generated multimodal instruction data, the codebase for data generation and model training, the model checkpoint, and a visible chat demo are open to the general public for access and could be accessed at https://github.com/haotian-liu/LLaVA. 

LLaVA has demonstrated impressive multimodal chat abilities and achieved an 85.1% relative rating compared with GPT-4 on an artificial multimodal instruction-following dataset. When fine-tuned on Science QA, LLaVA and GPT-4 synergy achieved a brand new SOTA accuracy of 92.53%. The outcomes make LLaVA a promising approach and an awesome contribution to the released language models. 

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