Summary of Smart Language Agents in Real-world Planning, by Annabelle Miin et al.
Smart Language Agents in Real-World Planning
by Annabelle Miin, Timothy Wei
First submitted to arxiv on: 29 Jul 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 A comprehensive planning agent, a long-term goal in AI, can be improved through Large Language Models (LLMs). Building upon TravelPlanner, this paper proposes a new method to enhance travel planning using LLMs. Specifically, the “sole-planning” mode is explored, where an agent creates a plan from reference information without simulating real-world scenarios. An optimization of this capability can still improve the user experience. A semi-automated prompt generation framework combines LLM-generated prompts with human input to refine and improve performance by 139% after a single iteration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re planning a trip, but instead of doing it yourself, you use an AI agent that helps you make decisions. This paper is about making this process better using special language models called Large Language Models (LLMs). The goal is to create a travel plan from scratch, without actually going through the real-world experience. By combining LLMs with human input, we can make the planning process more efficient and effective. |
Keywords
» Artificial intelligence » Optimization » Prompt
