Interactive and Expressive Code-Augmented Planning with Large Language Models
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arXiv
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| Format: | Preprint |
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2024
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| author | Liu, Anthony Z. Wang, Xinhe Sansom, Jacob Fu, Yao Choi, Jongwook Sohn, Sungryull Kim, Jaekyeom Lee, Honglak |
| author_facet | Liu, Anthony Z. Wang, Xinhe Sansom, Jacob Fu, Yao Choi, Jongwook Sohn, Sungryull Kim, Jaekyeom Lee, Honglak |
| contents | Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. These techniques include using variables (to track important information) and functions (to divide complex tasks into smaller re-usable sub-tasks). However, purely code-based approaches can be error-prone and insufficient for handling ambiguous or unstructured data. To address these challenges, we propose REPL-Plan, an LLM planning approach that is fully code-expressive (it can utilize all the benefits of code) while also being dynamic (it can flexibly adapt from errors and use the LLM for fuzzy situations). In REPL-Plan, an LLM solves tasks by interacting with a Read-Eval-Print Loop (REPL), which iteratively executes and evaluates code, similar to language shells or interactive code notebooks, allowing the model to flexibly correct errors and handle tasks dynamically. We demonstrate that REPL-Plan achieves strong results across various planning domains compared to previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13826 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interactive and Expressive Code-Augmented Planning with Large Language Models Liu, Anthony Z. Wang, Xinhe Sansom, Jacob Fu, Yao Choi, Jongwook Sohn, Sungryull Kim, Jaekyeom Lee, Honglak Computation and Language Machine Learning Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. These techniques include using variables (to track important information) and functions (to divide complex tasks into smaller re-usable sub-tasks). However, purely code-based approaches can be error-prone and insufficient for handling ambiguous or unstructured data. To address these challenges, we propose REPL-Plan, an LLM planning approach that is fully code-expressive (it can utilize all the benefits of code) while also being dynamic (it can flexibly adapt from errors and use the LLM for fuzzy situations). In REPL-Plan, an LLM solves tasks by interacting with a Read-Eval-Print Loop (REPL), which iteratively executes and evaluates code, similar to language shells or interactive code notebooks, allowing the model to flexibly correct errors and handle tasks dynamically. We demonstrate that REPL-Plan achieves strong results across various planning domains compared to previous methods. |
| title | Interactive and Expressive Code-Augmented Planning with Large Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.13826 |