ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation (2023.findings-emnlp)
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| Challenge: | Unlike previous methods, large language models produce entity-agnostic templates instead of copying the given example entities or validating/crafting the templates manually. |
| Approach: | They propose an approach for structured data verbalisation into short template sentences in zero to few-shot settings that prompts Large Language Models to directly produce entity-agnostic templates. |
| Outcome: | The proposed approach averages 66% parsing error rate reduction in generated verbalisations of RDF triples on the DART dataset. |
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