SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting

Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, Helen Meng

Abstract
Building and maintaining end-to-end task bots using minimal human effort is a long-standing challenge in dialog research. In this work, we introduce SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems effortlessly based on large language models (LLMs). Utilizing the predefined task schema, i.e., belief instruction and dialog policy, we instruct fixed LLMs to generate appropriate responses on novel tasks, without the need for training data. Specifically, SGP-TOD comprises three components: an LLM for interacting with users, a Dialog State Tracking (DST) Prompter to aid the LLM in tracking dialog states with the given belief instruction, and a Policy Prompter to direct the LLM to generate proper responses adhering to the provided dialog policy. Experimental results on Multiwoz, RADDLE, and STAR datasets show that our training-free strategy, SGP-TOD, yields state-of-the-art (SOTA) zero-shot performance, significantly surpassing the few-shot approaches. In a domain-extension setting, SGP-TOD aptly adapts to new functionalities by merely adding supplementary schema rules. We make our code and data publicly available.
Anthology ID:
2023.findings-emnlp.891
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13348–13369
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2023.findings-emnlp.891/
DOI:
10.18653/v1/2023.findings-emnlp.891
Bibkey:
Cite (ACL):
Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, and Helen Meng. 2023. SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13348–13369, Singapore. Association for Computational Linguistics.
Cite (Informal):
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting (Zhang et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/2023.findings-emnlp.891.pdf
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