Pranav Pillai


2024

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Dialog Flow Induction for Constrainable LLM-Based Chatbots
Stuti Agrawal | Pranav Pillai | Nishi Uppuluri | Revanth Gangi Reddy | Sha Li | Gokhan Tur | Dilek Hakkani-Tur | Heng Ji
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue

LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to ensure that these chatbots stay within the boundaries of the specialized domains, potentially resulting in inaccurate information and irrelevant responses. This paper introduces an unsupervised approach for automatically inducing domain-specific dialog flows that can be used to constrain LLM-based chatbots. We introduce two variants of dialog flow based on the availability of in-domain conversation instances. Through human and automatic evaluation over 24 dialog domains, we demonstrate that our high-quality data-guided dialog flows achieve better domain coverage, thereby overcoming the need for extensive manual crafting of such flows.