Summary of Dynamic Planning For Llm-based Graphical User Interface Automation, by Shaoqing Zhang et al.
Dynamic Planning for LLM-based Graphical User Interface Automation
by Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Xinbei Ma, Muyun Yang, Tiejun Zhao, Min Zhang
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 approach called Dynamic Planning of Thoughts (D-PoT) for large language models (LLMs)-based graphical user interface (GUI) agents. The goal is to improve the planning and action prediction in GUI tasks, particularly when dealing with dynamic environments. The traditional ReAct approach is shown to fail due to its reliance on excessive historical dialogue data. D-PoT addresses this challenge by dynamically adjusting plans based on environmental feedback and execution history. Experimental results demonstrate a significant improvement over the strong GPT-4V baseline (+12.7%, 34.66% → 47.36% in accuracy). The proposed approach also shows generality across different backbone LLMs, mitigates hallucinations, and adapts to unseen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer programs smarter. It’s like when you use a smartphone, and the program helps you do things like swipe left or right. The problem is that these programs need to figure out what to do next, which can be hard. The researchers propose a new way called Dynamic Planning of Thoughts (D-PoT) to make these programs better at planning and making decisions. They tested this idea and found it works really well! It’s like having a super smart personal assistant on your phone. |
Keywords
» Artificial intelligence » Gpt
