  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 language models
in generating natural language text from semantic triples. We compare four
large language models of varying sizes with different prompting techniques.
Through a series of benchmark experiments on the WebNLG dataset, we analyze the
models' performance and identify the most common issues in the generated
predictions. Our findings show that the capabilities of large language models
in triple verbalization can be significantly improved through few-shot
prompting, post-processing, and efficient fine-tuning techniques, particularly
for smaller models that exhibit lower zero-shot performance.
