Towards Developing an Agent-Based Framework for Validating the Trustworthiness of Large Language Models

Abstract

Large language models (LLMs) have revolutionized the field of generative artificial intelligence and strongly affect human-computer interaction based on natural language. Yet, it is difficult for users to understand how trustful LLM outputs are. Therefore, this paper develops an agent-based framework by exploring approaches, methods, and the integration of external data sources. The framework contributes to AI reasearch and usage by enabling future users to consider LLM outputs more efficiently and critically.