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.

翻译：尽管现有模型取得了显著进展，但从结构化数据输入生成文本描述（即数据到文本生成）仍然是一项具有挑战性的任务。在本文中，我们提出了一种新颖的方法，超越了传统的一次性生成方法，引入了一个包含生成、验证和纠正阶段的多步骤流程。我们的方法，即VCP（验证与纠正提示），首先让模型生成初始输出，然后对生成文本的不同方面进行正确性验证。验证步骤的观察结果被转化为专门的错误指示提示，该提示指导模型在考虑识别出的错误后重新生成输出。为了增强模型的纠正能力，我们开发了一种精心设计的训练过程。该过程使模型能够融入来自错误指示提示的反馈，从而改进输出生成。通过实验结果，我们证明该方法在保持生成文本整体质量的同时，有效降低了槽错误率。