  This work focuses on the novel problem setting of generating graphs
conditioned on a description of the graph's functional requirements in a
downstream task. We pose the problem as a text-to-text generation problem and
focus on the approach of fine-tuning a pretrained large language model (LLM) to
generate graphs. We propose an inductive bias which incorporates information
about the structure of the graph into the LLM's generation process by
incorporating message passing layers into an LLM's architecture. To evaluate
our proposed method, we design a novel set of experiments using publicly
available and widely studied molecule and knowledge graph data sets. Results
suggest our proposed approach generates graphs which more closely meet the
requested functional requirements, outperforming baselines developed on similar
tasks by a statistically significant margin.
