LLaVA is a multimodal AI from UC Davis and Microsoft Research that processes images and text together. It combines CLIP's vision encoder with Vicuna 13B language model, trained on GPT-4-generated instruction data. On ScienceQA's 21,208 multimodal questions, it reached 92.53% accuracy, though it still struggles with factual errors and safety issues.
Questions this video answers
UC Davis and Microsoft Research.
OpenAI CLIP.
Vicuna, 13 billion parameters (13B).
Synthetic multimodal instruction-following data generated by GPT-4 (plus user-generated ChatGPT+ data).
21,208 multimodal multiple-choice questions.
Chapters
So today we are talking about lava, an AI model that does more than just chat. It can actually see and understand pictures. And not just that. It can talk about what it sees, identify what's in the image, and even spot problems to offer solutions. Brought to you by the brilliant minds at UC Davis and Microsoft Research, this is definitely something you'll find both amazing and super interesting. To tackle the shortcomings of GPT4, the the research teams at UC Davis and Microsoft Research had an innovative idea. They wondered if they could use GPT4 to create training data that involves both text and pictures. For example, could they get GPT4 to generate tasks like label the parts of this flower or explain why this bridge is stable? Then could they use these generated tasks to train a new model that understands both words and images and can also carry out complicated instructions involving both? This is exactly the approach they took when creating lava. LAVA is made up of two main parts. One part for seeing pictures, called a Vision Encoder, and another part for understanding and generating words, known as a language decoder. The job of the Vision Encoder is to look at images and pick out important details that are needed for whatever task it's performing. On the other side, the language decoder takes that information along with any text instructions and turns it into a response you can understand. These two parts, the Vision Encoder and Language decoder, are linked together by something called an attention mechanism. This is what helps them talk to each other and share information back and forth. Now, the Vision Encoder is built on a really advanced image understanding model from OpenAI, known as CLIP. This model is really good at learning from both pictures and words. As for the language decoder, it's designed around Vicuna, a powerful language model with 13 billion parameters. The researchers used GPT4 to generate multimodal instruction following data by feeding it various prompts and instructions such as Draw me a picture of a cat or Write me a poem about this painting. They then used these data to train Lava using instruction tuning, a technique that allows the model to learn from machine generated data without human supervision. Instruction tuning works by optimizing the model's performance on the given instruction rather than on a specific output. For example, if the instruction is draw me a picture of a cat, the model does not need to produce exactly the same picture as GPT4. It just needs to produce a picture that looks like a cat. This way, the model can learn from diverse and creative examples generated by GPT4. The team developed lava focusing on three specific objectives. First, they wanted to apply the concept of instruction tuning to a realm where both text and images matter. That's what they call the multimodal space. Second, they aimed to develop sophisticated multimodal models, which means models that can perform complicated tasks involving both words and pictures. Finally, they set out to scientifically examine how well user generated data could work when tuning instructions in this multimodal environment. The great news is they successfully achieved all these objectives and the results are really impressive. First, they showed that instruction tuning can be successfully applied to the multimodal space, allowing LAVA to learn from synthetic tasks generated by GPT4 without human supervision. They also showed that instruction tuning can improve the model's performance on various benchmarks such as vqa, imagenet and coco. Second, they showed that LAVA is a powerful and versatile multimodal model that can handle complex tasks involving both text and images, such as multimodal chat, image captioning, image generation, image editing and image retrieval. They also showed that lava can outperform GPT4 on a synthetic multimodal instruction following dataset, achieving a relative score of 85.1%. Third, they showed that user generated data can be very useful for multimodal instruction tuning, especially when the data are diverse and creative. They collected user generated data from ChatGPT+ that allows users to interact with GPT4. They found that user generated data can improve Lava's performance on various tasks such as multimodal chat and image generation. One of the most impressive achievements of LAVA is is its state of the art performance on the ScienceQA dataset, a new benchmark that consists of 21,208 multimodal multiple choice questions with diverse science topics and annotations of their answers with corresponding lectures and explanations. The ScienceQA dataset is challenging because it requires the model to have general external knowledge and specific reasoning abilities to to answer the questions correctly. The researchers fine tuned lava on the ScienceQA dataset using both text and image inputs and achieved a new state of the art accuracy of 92.53% surpassing the previous best result by 6.5%. They also showed that LAVA can generate high quality lectures and explanations for its answers, demonstrating its multimodal reasoning and communication skills. The team made Lava publicly available on GitHub where you can find the code, the data, the models and the demos. While it might seem like it's only an interesting experiment, it's actually a tool with many practical uses. You can think of LAVA as a teaching assistant. It can help us learn about all kinds of topics by showing us both text and pictures and it can answer our questions with detailed lectures and explanations. It can also be your creative partner, aiding you in writing, drawing, or even design projects. It can work with you to create or fine tune all kinds of content. And if you're just looking for some fun, Lava can serve as an entertainment buddy, chatting with you about anything you like, from hobbies to favorite movies, and even showing you pictures or diagrams that add something extra to the conversation. Of course, Lava is not perfect. It's got a few things that still need sorting out. One issue is that it can sometimes give information that's not accurate or can be misleading, especially when you ask it specific or technical questions. This happens because Lava doesn't have a foolproof method for checking or explaining the answers it provides. It leans on its built in knowledge and reasoning abilities, which might not always be spot on. Another area that needs improvement is making sure Lava is safe and aligned with human values. At times it might generate content that's harmful or just not appropriate, particularly if the topic is a sensitive or a hot button issue. This is because Lava doesn't have a nuanced understanding of human ethics or societal norms. It's programmed to respond to the prompts it's given, but those responses aren't always in the best taste or the most responsible. Now, the good news is that the research team is fully aware of these hurdles. They're committed to improving LAVA by gathering more feedback from people and by using more external sources to make the system smarter. They're also going to make the model more secure and aligned with human expectations by applying stricter tests and ongoing monitoring. Their goal is to enhance Alava so that it's not only more reliable, but also a tool that we can trust and find genuinely useful. That brings us to the end of today's video. I hope you found it both enjoyable and educational. If you liked what you saw, I'd appreciate it if you could hit the thumbs up button and also subscribe to my channel for more content similar to this. If you have any questions or thoughts about Lava or anything else that's AI related, feel free to leave them in the comment section below. I'd love to hear from you. Thanks for watching and I'll see you in the next one.
