Guideline Compliance in Task-Oriented Dialogue: The Chained Prior Approach
Abstract: Task-oriented dialogue (TOD) systems are widely used across various domains, including customer service, appointment scheduling, and technical support. In real-world scenarios, such systems must adhere to given operational guidelines. However, existing solutions based on large language models often cannot achieve strict guideline compliance, even when fine-tuned with domain knowledge. To address this issue, we introduce a novel TOD system named GuidedTOD, which explicitly considers domain-specific guidelines by integrating a policy module. This module employs a Markov Chain, termed Chained Prior, to efficiently encode and dynamically update guideline knowledge. During inference, the Chained Prior re-ranks outputs from the domain-expert language model using beam search, ensuring guideline adherence. Experimental results show that GuidedTOD significantly improves guideline compliance, achieving approximately 20% better action prediction accuracy than state-of-the-art solutions.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: Task-oriented Dialogue, Markov Chain, Guideline Compliance, Large Language Model, In-context Learning, Reasoning
Contribution Types: Approaches to low-resource settings
Languages Studied: English
Submission Number: 3971
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