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We present ASPIRO, an approach for structured data verbalisation into shorttemplate sentences in zero to few-shot settings. Unlike previous methods, ourapproach prompts large language models (LLMs) to directly produceentity-agnostic templates, rather than relying on LLMs to faithfully copy thegiven example entities, or validating/crafting the templates manually. Weincorporate LLM re-prompting, triggered by algorithmic parsing checks, as wellas the PARENT metric induced consistency validation to identify and rectifytemplate generation problems in real-time. ASPIRO, compared to direct LLMoutput, averages 66\% parsing error rate reduction in generated verbalisationsof RDF triples on the DART dataset. Our best 5-shot text-davinci-003 setup,scoring BLEU of 50.62, METEOR of 45.16, BLEURT of 0.82, NUBIA of 0.87, andPARENT of 0.8962 on the Rel2Text dataset, competes effectively with recentfine-tuned pre-trained language models.

ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation