@inproceedings{chiu-etal-2023-symbolic-planning,
title = "Symbolic Planning and Code Generation for Grounded Dialogue",
author = "Chiu, Justin and
Zhao, Wenting and
Chen, Derek and
Vaduguru, Saujas and
Rush, Alexander and
Fried, Daniel",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.460",
doi = "10.18653/v1/2023.emnlp-main.460",
pages = "7426--7436",
abstract = "Large language models (LLMs) excel at processing and generating 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.",
}
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<abstract>Large language models (LLMs) excel at processing and generating 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.</abstract>
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%0 Conference Proceedings
%T Symbolic Planning and Code Generation for Grounded Dialogue
%A Chiu, Justin
%A Zhao, Wenting
%A Chen, Derek
%A Vaduguru, Saujas
%A Rush, Alexander
%A Fried, Daniel
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chiu-etal-2023-symbolic-planning
%X Large language models (LLMs) excel at processing and generating 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.
%R 10.18653/v1/2023.emnlp-main.460
%U https://aclanthology.org/2023.emnlp-main.460
%U https://doi.org/10.18653/v1/2023.emnlp-main.460
%P 7426-7436
Markdown (Informal)
[Symbolic Planning and Code Generation for Grounded Dialogue](https://aclanthology.org/2023.emnlp-main.460) (Chiu et al., EMNLP 2023)
ACL
- Justin Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru, Alexander Rush, and Daniel Fried. 2023. Symbolic Planning and Code Generation for Grounded Dialogue. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7426–7436, Singapore. Association for Computational Linguistics.