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.
翻译:在知识表示与推理以及自然语言处理领域,实现与机器类似的通信仍然是一个经典而富有挑战性的话题。这些大型语言模型(LLMs)依赖于模式匹配,而不是真正理解一句话的语义含义。因此,它们可能会生成不正确的响应。要生成确保正确的响应,必须“理解”一句话的语义。要实现这种“理解”,需要逻辑推理方法(如公共常识推理方法)的支持。本文介绍了AutoConcierge系统,该系统利用LLMs和ASP开发了一种对话代理,可以在受限领域中真正“理解”人类对话。AutoConcierge专注于特定领域,根据用户的偏好向其建议当地的餐厅。AutoConcierge将与用户的语言互动,确定其中缺失的信息,并通过自然语言句子要求用户提供它。一旦AutoConcierge确定已收到所有信息,它将根据从人类用户获得的用户偏好计算出餐厅推荐。AutoConcierge基于我们早期开发的STAR框架,该框架使用GPT-3将人类对话转换为捕获对话句子深层结构的谓词。然后,这些谓词作为目标驱动的s(CASP)ASP系统的输入,用于执行公共常识推理。据我们所知,AutoConcierge是第一个能够真实地像人类一样对话并根据对人类话语的真正理解来帮助人类的自动对话代理。