Papers by Anthony Zhe Liu
Interactive and Expressive Code-Augmented Planning with Large Language Models (2025.acl-long)
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Anthony Zhe Liu, Xinhe Wang, Jacob Sansom, Yao Fu, Jongwook Choi, Sungryull Sohn, Jaekyeom Kim, Honglak Lee
| Challenge: | Large Language Models (LLMs) have strong abilities in common-sense reasoning and interactive decision-making, but struggle with complex, long-horizon planning tasks. |
| Approach: | They propose a code-based LLM planning approach that is code-expressive while also dynamically adapting from errors. |
| Outcome: | The proposed approach can be error-prone and insufficient for handling ambiguous or unstructured data. |