用自动化对话模拟生成真实编程问答数据，提升代码问答模型性能。
DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production
- 模拟开发者在IDE中的多轮对话与上下文行为生成数据。
- 训练后模型回答接受率提升33%，效率比人工高4.8倍。
- 适合需要高质量代码问答数据的AI研发团队使用。
大型语言模型（LLMs）在代码生成、理解与修复任务中日益成为提升开发者效率的关键。然而，由于隐私顾虑及缺乏可访问的标注数据集，用高质量真实数据微调模型仍具挑战。本文提出DialogAgent，一种自动化工具，可生成高度仿真的合成训练数据，精准模拟集成开发环境（IDE）中的真实开发者交互。该工具通过模拟多轮对话和实际编程场景中的上下文行为，生成多样化、高保真的问答对。相比传统方法，其数据生产效率提升4.8倍。实验与线上部署结果表明，基于DialogAgent生成的数据训练的模型，在代码问答任务中表现显著提升：自研模型生成回答的接受率提高33%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have become increasingly integral to enhancing developer productivity, particularly in code generation, comprehension, and repair tasks. However, fine-tuning these models with high-quality, real-world data is challenging due to privacy concerns and the lack of accessible, labeled datasets. In this paper, we present DialogAgent, an automated tool for generating synthetic training data that closely mimics real developer interactions within Integrated Development Environments (IDEs). DialogAgent enables the production of diverse, high-fidelity query-response pairs by simulating multi-turn dialogues and contextual behaviors observed in real-world programming scenarios. The tool significantly reduces the reliance on manual data generation, increasing efficiency by 4.8 times compared to traditional methods. Our experiments and online deployment demonstrate substantial improvements in model performance for code-related question-answering tasks: the acceptance rate of responses generated by our in-house model is improved by 33%, after training on synthesized data generated by DialogAgent.
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