Title: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

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

View PDF Abstract: 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. Subjects: Machine Learning (cs.LG) ; Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2309.02591 [cs.LG] arXiv:2309.02591v1 [cs.LG] for this version)

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