Symbolic Planning and Code Generation for Grounded Dialogue
Justin T. Chiu Wenting Zhao Derek Chen Saujas Vaduguru Alexander M. Rush Daniel Fried
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.