  Conversational question answering systems often rely on semantic parsing to
enable interactive information retrieval, which involves the generation of
structured database queries from a natural language input. For
information-seeking conversations about facts stored within a knowledge graph,
dialogue utterances are transformed into graph queries in a process that is
called knowledge-based conversational question answering. This paper evaluates
the performance of large language models that have not been explicitly
pre-trained on this task. Through a series of experiments on an extensive
benchmark dataset, we compare models of varying sizes with different prompting
techniques and identify common issue types in the generated output. Our results
demonstrate that large language models are capable of generating graph queries
from dialogues, with significant improvements achievable through few-shot
prompting and fine-tuning techniques, especially for smaller models that
exhibit lower zero-shot performance.
