Bo_liu Llmp Empowering Large Language Models With Optimal Planning Proficiency 2023

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1. Title: LLM+P Empowering Large Language Models With Optimal Planning Proficiency
2. Author: Bo Liu
3. Publish Year: 5 May 2023
4. Review Date: Mon, May 22, 2023
5. url: https://arxiv.org/pdf/2304.11477.pdf

Summary of paper

Motivation

• However, so far, LLMs cannot reliably solve long-horizon planning problems. By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal plans.

Contribution

• introduce LLM+P, it takes in a natural language description of a planning problem, then return a correct plan for solving that problem in natural language. LLM+P does so by first converting the language description into a file written in the planning domain definition language (PDDL)
• limitation of the paper: In this paper, we do not ask the LLM to recognize that it has been posed a prompt that is suitable for processing using the proposed LLM+P pipeline.

Some key terms

limitation of LLMs

• LLMs have become amazingly proficient at linguistic competence — knowing how to say things; but they are not nearly as good at functional competence — knowing what to say.
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Architecture

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• assumptions

  • A chatbot knows when to trigger LLM+P based on its conversation with a human user
  • A domain PDDL file is provided for the problem the user asks for
  • A simple problem description in natural language and its corresponding problem PDDL file are also provided beforehand. (used as context)

Results

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Potential future work

• ALFRED contains the exploration process. how can ensure that all the required objects are found during exploration