Documentation debt hinders the effective utilization of open-source software.
Although code summarization tools have been helpful for developers, most would
prefer a detailed account of each parameter in a function rather than a
high-level summary. However, generating such a summary is too intricate for a
single generative model to produce reliably due to the lack of high-quality
training data. Thus, we propose a multi-step approach that combines multiple
task-specific models, each adept at producing a specific section of a
docstring. The combination of these models ensures the inclusion of each
section in the final docstring. We compared the results from our approach with
existing generative models using both automatic metrics and a human-centred
evaluation with 17 participating developers, which proves the superiority of
our approach over existing methods.

文档义务阻碍了开源软件的有效利用。为了解决单一生成模型无法可靠产生详细函数参数的问题，我们提出了一种多步骤的方法，结合多个任务特定模型，每个模型都能产生 docstring 的特定部分，这些模型的组合确保了最终 docstring 中每个部分的包含。通过与现有的生成模型进行比较，我们的方法结果使用自动度量和 17 位开发者的人为评估，证明了我们方法的优越性。

DocGen: 在 Python 中生成详细的参数文档字符串

DocGen: Generating Detailed Parameter Docstrings in Python

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。