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


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.

[Read Full Article](https://arxiv.org/abs/2402.01018)