  Large language models (LLMs) have demonstrated impressive abilities in
generating unstructured natural language according to instructions. However,
their performance can be inconsistent when tasked with producing text that
adheres to specific structured formats, which is crucial in applications like
named entity recognition (NER) or relation extraction (RE). To address this
issue, this paper introduces an efficient method, G&O, to enhance their
structured text generation capabilities. It breaks the generation into a
two-step pipeline: initially, LLMs generate answers in natural language as
intermediate responses. Subsequently, LLMs are asked to organize the output
into the desired structure, using the intermediate responses as context. G&O
effectively separates the generation of content from the structuring process,
reducing the pressure of completing two orthogonal tasks simultaneously. Tested
on zero-shot NER and RE, the results indicate a significant improvement in LLM
performance with minimal additional efforts. This straightforward and adaptable
prompting technique can also be combined with other strategies, like
self-consistency, to further elevate LLM capabilities in various structured
text generation tasks.
