Summary of Lasp: Surveying the State-of-the-art in Large Language Model-assisted Ai Planning, by Haoming Li and Zhaoliang Chen and Jonathan Zhang and Fei Liu
LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning
by Haoming Li, Zhaoliang Chen, Jonathan Zhang, Fei Liu
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A novel survey explores the challenges in planning with language models (LLMs), highlighting their capabilities in commonsense reasoning for deducing action sequences and identifying effective courses. However, it is often observed that plans generated through direct prompting fail upon execution. The survey focuses on key areas such as embodied environments, optimal scheduling, competitive and cooperative games, task decomposition, reasoning, and planning. This study provides unique insights into the future of LM-assisted planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Planning is a crucial process for achieving goals. Language models (LLMs) are great at helping with this by making smart decisions. They can figure out what actions to take to reach a goal from where you start. But sometimes, plans made by LLMs don’t work when put into action. This study looks at the problems with planning using LLMs and how they can help in the future. |
Keywords
» Artificial intelligence » Prompting
