Summary of How to Build a Pre-trained Multimodal Model For Simultaneously Chatting and Decision-making?, by Zuojin Tang et al.
How to Build a Pre-trained Multimodal model for Simultaneously Chatting and Decision-making?
by Zuojin Tang, Bin Hu, Chenyang Zhao, De Ma, Gang Pan, Bin Liu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 a novel pre-trained model architecture called Visual Language Action model for Chatting and Decision Making (VLA4CD) that combines language interaction with precise decision-making capabilities in dynamic open scenarios. Unlike existing models like ChatGPT or OpenVLA, which typically map text input to text output or action decisions, VLA4CD can generate text responses while providing continuous-valued action decisions. The model leverages LoRA to fine-tune a pre-trained LLM with data from multiple modalities, including language, visual, and action. This enables VLA4CD to provide more accurate real-time decision-making while retaining the text interaction capability inherent to LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of AI model that can both have a conversation with you and make smart decisions. For example, it could help an autonomous car drive safely while talking to its passengers. The researchers built this model by combining two types of information: what someone says (language) and the actions they take (like pressing a button). They tested their model on a virtual driving simulator and found that it was better at making decisions than other models that only focused on one type of information. |
Keywords
» Artificial intelligence » Lora
