HR-MultiWOZ：面向HR大语言模型智能体的任务导向对话（TOD）数据集
HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
AI总结：
针对HR领域LLM智能体缺乏高质量标注对话数据集、现有数据集不匹配员工需求且收集真实对话有隐私风险的问题，提出首个HR领域开源标注对话数据集HR-Multiwoz，含550段对话覆盖10个领域，还给出可迁移的低人力成本数据生成流水线。
摘要：
大语言模型（LLM）的最新进展正在多个领域重塑自然语言处理（NLP）任务，其在人力资源（HR）领域的应用仍有拓展空间，可助力多项耗时任务，例如请假提交、医疗理赔申报、权限申请等值得关注的场景，但这类应用远不止这些。不过，上述发展需应对构建高质量训练数据集这一核心挑战：一方面，现有多数对话数据集解决的是客户而非员工的问题；另一方面，收集与HR的对话可能引发隐私担忧。为解决该问题，我们提出HR-Multiwoz——一个包含550段对话、覆盖10个HR领域的全标注数据集，用于评估LLM Agent。本工作贡献如下：（1）它是首个面向NLP研究的HR领域标注开源对话数据集。（2）提供了详细的数据生成流程方案，附数据分析与人工评估结果，该数据生成流水线具备可迁移性，可轻松适配其他领域的标注对话数据生成。（3）所提出的数据收集流水线主要基于LLM，仅需最少的人工标注参与，兼具时间与成本效率。
英文摘要：
Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) has still room for expansions and could be beneficial for several time consuming tasks. Examples such as time-off submissions, medical claims filing, and access requests are noteworthy, but they are by no means the sole instances. However, the aforementioned developments must grapple with the pivotal challenge of constructing a high-quality training dataset. On one hand, most conversation datasets are solving problems for customers not employees. On the other hand, gathering conversations with HR could raise privacy concerns. To solve it, we introduce HR-Multiwoz, a fully-labeled dataset of 550 conversations spanning 10 HR domains to evaluate LLM Agent. Our work has the following contributions: (1) It is the first labeled open-sourced conversation dataset in the HR domain for NLP research. (2) It provides a detailed recipe for the data generation procedure along with data analysis and human evaluations. The data generation pipeline is transferable and can be easily adapted for labeled conversation data generation in other domains. (3) The proposed data-collection pipeline is mostly based on LLMs with minimal human involvement for annotation, which is time and cost-efficient.
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。
