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.

本文提出了一个多步骤方法，通过引入验证、纠错和生成阶段，超越传统的一次性生成方法，实现了从结构化输入生成文本描述，名为数据到文本生成。我们的方法使用一个验证步骤，将观察结果转换成特殊的错误指示提示，指导模型修复错误并改进输出，通过实验结果展示了我们的方法有效减少了槽错误率，并保持了生成文本的总体质量。