A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP

TL;DR

This paper introduces AutoCompanion, a socialbot combining large language models with goal-directed Answer Set Programming to ensure coherent, goal-oriented, and correct social conversations about movies and books.

Contribution

It presents a novel framework integrating LLMs with ASP for controlled, goal-driven social interactions, addressing LLM limitations in coherence and topic maintenance.

Findings

s(CASP) ensures correctness of answers

The system maintains coherence and focus during conversations

AutoCompanion effectively keeps users engaged on specific topics

Abstract

The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on Answer Set Programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic.