Summary of Interactive and Expressive Code-augmented Planning with Large Language Models, by Anthony Z. Liu et al.
Interactive and Expressive Code-Augmented Planning with Large Language Models
by Anthony Z. Liu, Xinhe Wang, Jacob Sansom, Yao Fu, Jongwook Choi, Sungryull Sohn, Jaekyeom Kim, Honglak Lee
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 research proposes a novel approach called REPL-Plan for large language models (LLMs) to excel in complex planning tasks. The current LLMs show promise in common-sense reasoning and interactive decision-making, but struggle with long-horizon planning. Recent techniques have tried to structure LLM outputs using control flow and code-adjacent methods, which while effective, can be error-prone when dealing with ambiguous data. REPL-Plan addresses this by utilizing a Read-Eval-Print Loop (REPL) that iteratively executes and evaluates code, allowing the model to adapt and correct errors dynamically. The approach achieves strong results across various planning domains compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better at complex tasks like planning. Right now, these language models are good at solving problems and making decisions, but they struggle with long-term plans. Scientists have tried using special techniques to make them better, but it’s not perfect. They propose a new way called REPL-Plan that lets the computer model correct mistakes and handle unexpected situations. This helps the computer do better planning tasks. |
