Multimodal LLMs contain an encoder, LLM, and a “connector” between the multiple modalities. The LLM is typically pre-trained. For instance, LLaVA uses the CLIP ViT-L/14 for an image encoder and Vicuna ...

It uses Vicuna as the large language model (LLM) and CLIP ViT-L/14 as a visual encoder, which, for those unaware, has been developed by OpenAI. The project has generated high-quality multimodal ...

The main purpose of multimodal machine translation (MMT) is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in ...

NExT-GPT, an end-to-end MM-LLM, overcomes limitations of input-only multimodal understanding by integrating multimodal adaptors and diffusion decoders. This allows content processing and generation ...

This document provides a detailed, educational guide to designing and training an 88 billion parameter (88B) multimodal LLM capable of processing text, images, audio, PDFs, and other file types. We'll ...

In order to overcome the drawback of decoder-only LLMs for text embedding, a team of researchers from Mila, McGill University, ServiceNow Research, and Facebook CIFAR AI Chair has proposed LLM2Vec, a ...