Summary of Multimodal Latent Language Modeling with Next-token Diffusion, by Yutao Sun et al.
Multimodal Latent Language Modeling with Next-Token Diffusion
by Yutao Sun, Hangbo Bao, Wenhui Wang, Zhiliang Peng, Li Dong, Shaohan Huang, Jianyong Wang, Furu Wei
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Latent Language Modeling (LatentLM), a unified framework for handling both discrete and continuous data in multimodal generative models. Specifically, the authors combine causal Transformers with variational autoencoders (VAEs) to represent continuous data as latent vectors, while introducing next-token diffusion for autoregressive generation. They also develop σ-VAE to address variance collapse challenges. The authors demonstrate LatentLM’s effectiveness across various modalities, including image and text-to-speech synthesis. In image generation, LatentLM outperforms Diffusion Transformers in performance and scalability. When integrated into multimodal large language models, LatentLM provides a general-purpose interface for multimodal generation and understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Latent Language Modeling is a new way to create images and sounds using artificial intelligence. Imagine being able to generate realistic pictures or videos of anything you can imagine! This paper shows how to do just that by combining different types of data, like words and images. The authors created a special kind of computer model called LatentLM that can handle both discrete (words) and continuous (images) data at the same time. They tested it on various tasks, including generating new images and converting text into spoken language. The results show that LatentLM is better than other methods in many ways, like requiring fewer computing resources or producing more realistic sounds. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Image generation » Token
