Reliable Conversational Agents under ASP Control that Understand Natural Language

Conversational agents are designed to understand dialogs and generate meaningful responses to communicate with humans. After the popularity of ChatGPT, with its surprising performance and powerful conversational ability, commercial Large Language Models (LLMs) for general NLP tasks such as GPT-4 [1], etc., sprung up and brought the generative AI as a solution to the public view. These LLMs work quite well in content generation tasks, but their deficiency in fact-and-knowledge-oriented tasks is wellestablished by now [13]. These models themselves cannot tell whether the text they generate is based on facts or made-up stories, and they cannot always follow the given data and rules strictly and sometimes even modify the data at will, also called hallucination. The reasoning that these LLMs appear to perform is also at a very shallow level.