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

TL;DR

ChatHome is a specialized language model tailored for home renovation, combining domain-adaptive pretraining and instruction-tuning to deliver precise, high-fidelity outputs in this complex field.

Contribution

The paper introduces ChatHome, a novel domain-specific language model for home renovation, developed through a unique combination of domain-adaptive pretraining and instruction-tuning on extensive, relevant datasets.

Findings

Enhanced domain-specific performance on home renovation tasks

Maintains versatility across general and specialized datasets

Outperforms baseline models in accuracy and relevance

Abstract

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.