Over the course of the recent decade, tremendous progress has been made in
the areas of machine learning and natural language processing, which opened up
vast areas of potential application use cases, including hiring and human
resource management. We review the use cases for text analytics in the realm of
human resources/personnel management, including actually realized as well as
potential but not yet implemented ones, and we analyze the opportunities and
risks of these.

近十年来，在机器学习和自然语言处理领域取得了巨大进展，为招聘和人力资源管理等潜在应用领域开辟了广阔的前景。我们回顾了文本分析在人力资源 / 人事管理领域中的用例，包括已实现和潜在但尚未实施的用例，并分析了其中的机会和风险。

人力资源应用的自然语言处理挑战与机遇：一篇讨论文

Challenges and Opportunities of NLP for HR Applications: A Discussion  Paper

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.

我们介绍了 HR-Multiwoz，这是一个包含 550 个涵盖 10 个 HR 领域的完全标记的对话数据集，以评估 LLM Agent。该数据集是为自然语言处理 (NLP) 研究的 HR 领域首个开源对话数据集。通过 LLMs 为基础的数据收集流程，大大减少了人员参与的工作量和成本。

HR-MultiWOZ: 面向任务的对话 (TOD) 数据集用于 HR LLM 代理

HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent

Job titles form a cornerstone of today's human resources (HR) processes.
Within online recruitment, they allow candidates to understand the contents of
a vacancy at a glance, while internal HR departments use them to organize and
structure many of their processes. As job titles are a compact, convenient, and
readily available data source, modeling them with high accuracy can greatly
benefit many HR tech applications. In this paper, we propose a neural
representation model for job titles, by augmenting a pre-trained language model
with co-occurrence information from skill labels extracted from vacancies. Our
JobBERT method leads to considerable improvements compared to using generic
sentence encoders, for the task of job title normalization, for which we
release a new evaluation benchmark.

通过将预训练语言模型与从招聘信息中提取的技能标签协同出现信息相结合，我们提出了一种职位称号的神经表示模型， JobBERT 方法相比使用通用语句编码器，在职位标题规范化任务中表现出显著提高，并发布了一个新的评估基准。