Scaling Autoregressive Multi-modal Models: Pretraining And Instruction Tuning

Yu Lili, Shi Bowen, Pasunuru Ramakanth, Muller Benjamin, Golovneva Olga, Wang Tianlu, Babu Arun, Tang Binh, Karrer Brian, Sheynin Shelly, Ross Candace, Polyak Adam, Howes Russell, Sharma Vasu, Xu Puxin, Tamoyan Hovhannes, Ashual Oron, Singer Uriel, Li Shang-wen, Zhang Susan, James Richard, Ghosh Gargi, Taigman Yaniv, Fazel-zarandi Maryam, Celikyilmaz Asli, Zettlemoyer Luke, Aghajanyan Armen. Arxiv 2023

[Paper]    
Applications Fine Tuning GPT Language Modeling Model Architecture Pretraining Methods RAG Training Techniques

We present CM3Leon (pronounced “Chameleon”), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pre-training stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-the-art performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation.

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