This paper presents the development and evaluation of ChatHome, a
domain-specific language model (DSLM) designed for the intricate field of home
renovation. Considering the proven competencies of large language models (LLMs)
like GPT-4 and the escalating fascination with home renovation, this study
endeavors to reconcile these aspects by generating a dedicated model that can
yield high-fidelity, precise outputs relevant to the home renovation arena.
ChatHome's novelty rests on its methodology, fusing domain-adaptive pretraining
and instruction-tuning over an extensive dataset. This dataset includes
professional articles, standard documents, and web content pertinent to home
renovation. This dual-pronged strategy is designed to ensure that our model can
assimilate comprehensive domain knowledge and effectively address user
inquiries. Via thorough experimentation on diverse datasets, both universal and
domain-specific, including the freshly introduced "EvalHome" domain dataset, we
substantiate that ChatHome not only amplifies domain-specific functionalities
but also preserves its versatility.

本文介绍了 ChatHome，一种专门设计用于家庭装修领域的领域特定语言模型（DSLM）的开发和评估。通过对广泛的数据集进行域自适应预训练和指令调整的方法，ChatHome 能够生成与家庭装修相关的高保真度、精确的输出，既增强了领域特定功能，又保持了其通用性。