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Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs

2023-03-15 21:10:33
Yankai Zeng, Abhiramon Rajasekharan, Parth Padalkar, Kinjal Basu, Joaquín Arias, Gopal Gupta

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.

Abstract (translated)

与机器实现类似的方式进行通信仍然是知识表示和推理与自然语言处理领域的经典挑战性话题。这些大型语言模型(LLMs)依赖匹配模式而不是真正理解句子语义的含义。因此,它们可能会生成错误的回答。要生成确保准确的回答,你必须“理解”句子的语义。为了实现“理解”,基于逻辑(常识)推理方法,如答案集编程(ASP)等方法可能是必要的。在本文中,我们描述了自动售货机系统,它利用LLMs和ASP开发了一个能够在特定领域真正“理解”人类对话的交互式代理。自动售货机专注于一个特定领域,根据用户的偏好向用户推荐当地餐馆。自动售货机将 interactively 理解用户的言语,识别其中的缺失信息,并通过自然语言句子请求用户提供它。一旦自动售货机确定了所有信息都已接收,它将根据从人类用户的偏好获取的用户偏好计算一个餐馆推荐。据我们所知,自动售货机是第一个能够在真正理解人类言语的基础上像人类一样对话并为人类提供帮助的自动化对话代理。

URL

https://arxiv.org/abs/2303.08941

PDF

https://arxiv.org/pdf/2303.08941.pdf


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