Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs

• URL: http://arxiv.org/abs/2303.08941v1
• Date: Wed, 15 Mar 2023 21:10:33 GMT
• Title: Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
• Authors: Yankai Zeng and Abhiramon Rajasekharan and Parth Padalkar and Kinjal Basu and Joaqu\'in Arias and Gopal Gupta
• Abstract summary: Large Language Models (LLMs) rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. To generate an assuredly correct response, one has to "understand" the semantics of a sentence. We describe the AutoConcierge system that leverages ASP to develop a conversational agent that can truly "understand" human dialogs.
• Score: 4.212937192948915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These Large Language Models (LLMs) rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.

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