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

ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation