Summary of Anygpt: Unified Multimodal Llm with Discrete Sequence Modeling, by Jun Zhan et al.
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
by Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yugang Jiang, Xipeng Qiu
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces AnyGPT, a multimodal language model that can process various modalities such as speech, text, images, and music. Unlike other large language models (LLMs), AnyGPT does not require architectural changes or training paradigm alterations, instead relying on data-level preprocessing to seamlessly integrate new modalities. The authors create a multimodal text-centric dataset for alignment pre-training and synthesize a large-scale any-to-any multimodal instruction dataset comprising 108k multi-turn conversations. Experimental results show that AnyGPT can facilitate any-to-any multimodal conversation while achieving comparable performance to specialized models across all modalities, demonstrating the effectiveness of discrete representations in unifying multiple modalities within a language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to understand and generate text, speech, images, and music together. It creates a new way for machines to communicate with humans using different forms like chatbots or virtual assistants. The authors make it work by preparing the data in a special way so that big language models can easily learn from it. They also create a huge dataset of conversations between humans and computers that can handle any mix of text, speech, images, and music. This technology is important because it could lead to more natural interactions between humans and machines. |
Keywords
* Artificial intelligence * Alignment * Language model
