The emergence of large language models (LLMs) has revolutionized natural
language processing tasks. However, existing instruction-tuning datasets suffer
from occupational bias: the majority of data relates to only a few occupations,
which hampers the instruction-tuned LLMs to generate helpful responses to
professional queries from practitioners in specific fields. To mitigate this
issue and promote occupation-inclusive LLMs, we create an instruction-tuning
dataset named \emph{OccuQuest}, which contains 110,000+ prompt-completion pairs
and 30,000+ dialogues covering over 1,000 occupations in 26 occupational
categories. We systematically request ChatGPT, organizing queries
hierarchically based on Occupation, Responsibility, Topic, and Question, to
ensure a comprehensive coverage of occupational specialty inquiries. By
comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we
observe that OccuQuest exhibits a more balanced distribution across
occupations. Furthermore, we assemble three test sets for comprehensive
evaluation, an occu-test set covering 25 occupational categories, an estate set
focusing on real estate, and an occu-quora set containing real-world questions
from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which
significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and
WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on
the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against
WizardLM.

存在职业偏见的现有指导调整数据集限制了指导调整型大语言模型在特定领域的从业者的职业问题中生成有用的回应，为了缓解这个问题并推动包含各种职业的大语言模型的发展，我们创建了一个名为 OccuQuest 的指导调整数据集，包括超过 1,000 个职业在内的 110,000 多个提示完成对以及 30,000 多个对话，通过与三个常用数据集 (Dolly、ShareGPT 和 WizardLM) 进行比较，我们观察到 OccuQuest 在职业分布方面更加平衡，此外，我们还组装了三个用于综合评估的测试集，一个覆盖 25 个职业类别的 occu-test 测试集，一个聚焦于房地产的 estate 测试集和一个包含来自 Quora 的实际问题的 occu-quora 测试集，然后我们在 OccuQuest 上对 LLaMA 进行微调，得到 OccuLLaMA，在 GPT-4 和人工评估中，OccuLLaMA 在专业问题上明显优于现有的 LLaMA 变体 (Vicuna、Tulu 和 WizardLM)，值得注意的是，在 occu-quora 测试集上，OccuLLaMA 对 WizardLM 的胜率高达 86.4%。