Reliable Conversational Agents under ASP Control that Understand Natural Language
Efforts have been made to make machines converse like humans in the past few decades. The recent techniques of Large Language Models (LLMs) make it possible to have human-like conversations with machines, but LLM's flaws of lacking understanding and reliability are well documented. We believe that the best way to eliminate this problem is to use LLMs only as parsers to translate text to knowledge and vice versa and carry out the conversation by reasoning over this knowledge using the answer set programming. I have been developing a framework based on LLMs and ASP to realize reliable chatbots that "understand" human conversation. This framework has been used to develop task-specific chatbots as well as socialbots. My future research is focused on making these chatbots scalable and trainable.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets
Large Language Models can carry out human-like conversations in diverse settings, responding to user requests for tasks and knowledge. However, existing conversational agents implemented with LLMs often struggle with hal…
HallucinationNavigateEmpathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational Agents
We introduce the concept of "empathic grounding" in conversational agents as an extension of Clark's conceptualization of grounding in conversation in which the grounding criterion includes listener empathy for the speak…
Emotional IntelligenceEmotion ClassificationHuman Interaction RecognitionLanguage Modelling+4Why and When: Understanding System Initiative during Conversational Collaborative Search
In the last decade, conversational search has attracted considerable attention. However, most research has focused on systems that can support a \emph{single} searcher. In this paper, we explore how systems can support \…
Conversational SearchA Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is subst…
Spoken Language Understanding$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs…
AI Agent