A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation

Generating natural language text from graph-structured data is essential for conversational information seeking. Semantic triples derived from knowledge graphs can serve as a valuable source for grounding responses from conversational agents by providing a factual basis for the information they communicate. This is especially relevant in the context of large language models, which offer great potential for conversational interaction but are prone to hallucinating, omitting, or producing conflicting information. In this study, we conduct an empirical analysis of conversational large …

agents analysis comparative analysis context conversational conversational agents cs.cl data graph graphs information knowledge knowledge graphs language language models large language large language models natural natural language responses semantic serve structured data text text generation the information