ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation
This work addresses the need for accurate AI assistance in home renovation, but it is incremental as it applies existing methods like domain-adaptive pretraining and instruction-tuning to a new domain.
The paper tackled the problem of generating precise outputs for home renovation by developing ChatHome, a domain-specific language model, which improved domain-specific functionalities while maintaining versatility as demonstrated through evaluation on diverse datasets including the new 'EvalHome' dataset.
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.