Summary of Planning in the Dark: Llm-symbolic Planning Pipeline Without Experts, by Sukai Huang et al.
Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts
by Sukai Huang, Nir Lipovetzky, Trevor Cohn
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper proposes a novel approach to solve natural language-described planning tasks using Large Language Models (LLMs). The authors aim to overcome limitations of direct LLM use, such as inconsistent reasoning and hallucination. They introduce a hybrid pipeline that generates multiple action schema candidates and ranks them semantically without expert intervention. Experimental results show the proposed pipeline outperforms direct LLM planning. This fully automated end-to-end planner opens up AI planning for a broader audience with minimal domain expertise requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers smarter at understanding human language. Right now, computers have trouble following instructions written in words, and they often come up with silly or unrealistic solutions. The authors want to change this by creating a new way for computers to understand natural language and generate plans without needing humans to tell them what to do. They’re trying to make a computer system that can figure out what you mean when you write something, and then use that understanding to create a plan. This could be very useful in many areas, like helping robots or self-driving cars make decisions. |
Keywords
» Artificial intelligence » Hallucination
