Summary of Controllable Navigation Instruction Generation with Chain Of Thought Prompting, by Xianghao Kong et al.
Controllable Navigation Instruction Generation with Chain of Thought Prompting
by Xianghao Kong, Jinyu Chen, Wenguan Wang, Hang Su, Xiaolin Hu, Yi Yang, Si Liu
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 C-Instructor is a novel approach to instruction generation that leverages the capabilities of Large Language Models (LLMs). The proposed method utilizes chain-of-thought-style prompts for style-controllable and content-controllable instruction generation. This is achieved through the Chain of Thought with Landmarks (CoTL) mechanism, which identifies key landmarks and generates complete instructions. CoTL enhances the accessibility and controllability of generated instructions. Additionally, a Spatial Topology Modeling Task facilitates understanding of the spatial structure of the environment. A Style-Mixed Training policy enables style control for instruction generation based on different prompts within a single model instance. The proposed approach outperforms previous methods in text metrics, navigation guidance evaluation, and user studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C-Instructor is a new way to generate instructions that uses special language models. Right now, there are limits to what these models can do, but this new method lets you control the style and content of the generated instructions. It also helps the model understand spatial relationships in the environment, which makes it more useful for navigation and other tasks. The approach is tested with different methods and shows better results than previous attempts. |
