You Can Generate It Again: Data-to-text Generation with Verification and
  Correction Prompting
Xuan Ren Lingqiao Liu
Despite significant advancements in existing models, generating text
descriptions from structured data input, known as data-to-text generation,
remains a challenging task. In this paper, we propose a novel approach that
goes beyond traditional one-shot generation methods by introducing a multi-step
process consisting of generation, verification, and correction stages. Our
approach, VCP(Verification and Correction Prompting), begins with the model
generating an initial output. We then proceed to verify the correctness of
different aspects of the generated text. The observations from the verification
step are converted into a specialized error-indication prompt, which instructs
the model to regenerate the output while considering the identified errors. To
enhance the model's correction ability, we have developed a carefully designed
training procedure. This procedure enables the model to incorporate feedback
from the error-indication prompt, resulting in improved output generation.
Through experimental results, we demonstrate that our approach effectively
reduces slot error rates while maintaining the overall quality of the generated
text.