Large language models (LLMs) excel at processing and generating both text and
code. However, LLMs have had limited applicability in grounded task-oriented
dialogue as they are difficult to steer toward task objectives and fail to
handle novel grounding. We present a modular and interpretable grounded
dialogue system that addresses these shortcomings by composing LLMs with a
symbolic planner and grounded code execution. Our system consists of a reader
and planner: the reader leverages an LLM to convert partner utterances into
executable code, calling functions that perform grounding. The translated
code's output is stored to track dialogue state, while a symbolic planner
determines the next appropriate response. We evaluate our system's performance
on the demanding OneCommon dialogue task, involving collaborative reference
resolution on abstract images of scattered dots. Our system substantially
outperforms the previous state-of-the-art, including improving task success in
human evaluations from 56% to 69% in the most challenging setting.

使用大型语言模型与符号规划器和基于代码执行的系统组成的模块化可解释的对话系统，在处理任务导向对话中解决了大型语言模型难于引导到任务目标和处理新颖基础的问题，并在具有抽象散点图的协作参考解决任务中显著优于之前的最新技术，包括在最具挑战性的情境中将人工评估的任务成功率从 56% 提高到 69%。