DialogAgent：面向代码问答数据生产的自动参与智能体
DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production
AI总结：
针对代码问答模型微调缺乏高质量标注数据的问题，提出DialogAgent自动化工具，通过模拟IDE中真实开发者多轮交互生成高保真训练数据，效率提升4.8倍，使自研模型响应接受率提高33%。
摘要：
大语言模型（LLMs）在提升开发者生产力方面的作用日益凸显，尤其在代码生成、理解和修复任务中。然而，由于隐私问题以及缺乏可获取的标注数据集，使用高质量的真实世界数据对这些模型进行微调颇具挑战。本文提出DialogAgent——一款自动化工具，用于生成高度模拟集成开发环境（IDE）中真实开发者交互的合成训练数据。DialogAgent通过模拟真实编程场景中观察到的多轮对话和上下文行为，能够生成多样化、高保真的查询-响应对。该工具大幅降低了对人工数据生成的依赖，与传统方法相比效率提升4.8倍。我们的实验和线上部署表明，代码相关问答任务的模型性能得到显著提升：在使用DialogAgent生成的合成数据训练后，我们自研模型生成的响应接受率提高了33%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
