用大模型打造专用于人力资源的对话代理，自动处理重复性事务
HR-Agent: A Task-Oriented Dialogue (TOD) LLM Agent Tailored for HR Applications
- 构建面向HR场景的专用对话智能体，支持任务导向交互
- 对话数据本地处理，保障医疗、权限等敏感信息不外泄
- 适用于企业自动化人力流程，如请假、报销、权限申请
近期大语言模型（LLM）在教育、金融等领域取得显著进展，但人力资源领域仍存在大量重复性流程，如访问请求、医疗理赔和休假申请等，尚未得到解决。本文将LLM代理技术应用于此类任务，提出一种高效、安全且专为人力资源设计的任务导向对话系统——HR-Agent。该系统可自动处理医疗理赔、权限申请等常见流程。由于对话数据在推理过程中不上传至外部LLM，有效保障了人力资源相关操作的隐私与合规性要求。
原文摘要 · Abstract (English)
Recent LLM (Large Language Models) advancements benefit many fields such as education and finance, but HR has hundreds of repetitive processes, such as access requests, medical claim filing and time-off submissions, which are unaddressed. We relate these tasks to the LLM agent, which has addressed tasks such as writing assisting and customer support. We present HR-Agent, an efficient, confidential, and HR-specific LLM-based task-oriented dialogue system tailored for automating repetitive HR processes such as medical claims and access requests. Since conversation data is not sent to an LLM during inference, it preserves confidentiality required in HR-related tasks.
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