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

arXiv: https://arxiv.org/abs/2307.15290 · HTML: https://mlsanity.com/paper/2307.15290/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)
- [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://mlsanity.com/paper/2111.10952/) (2021)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://mlsanity.com/paper/2306.16092/) (2023)
- [ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation](https://mlsanity.com/paper/2306.09968/) (2023)

## Built on by (most cited first, 25 of 25)

- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning](https://mlsanity.com/paper/2402.11260/) (2024)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications](https://mlsanity.com/paper/2405.19266/) (2024)
- [DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modeling](https://mlsanity.com/paper/2403.01197/) (2024)
- [CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfer](https://mlsanity.com/paper/2401.05707/) (2024)
- [EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data](https://mlsanity.com/paper/2312.15696/) (2023)
- [OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety](https://mlsanity.com/paper/2403.12316/) (2024)
- [CTourLLM: Enhancing LLMs with Chinese Tourism Knowledge](https://mlsanity.com/paper/2407.12791/) (2024)
- [RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services](https://mlsanity.com/paper/2507.10605/) (2025)
- [Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse](https://mlsanity.com/paper/2403.09167/) (2024)
- [Redefining Information Retrieval of Structured Database via Large Language Models](https://mlsanity.com/paper/2405.05508/) (2024)
- [Investigating Public Fine-Tuning Datasets: A Complex Review of Current Practices from a Construction Perspective](https://mlsanity.com/paper/2407.08475/) (2024)
- [Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data](https://mlsanity.com/paper/2412.01078/) (2024)
- [PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts](https://mlsanity.com/paper/2503.06706/) (2025)
- [DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management](https://mlsanity.com/paper/2505.15856/) (2025)
- [Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges](https://mlsanity.com/paper/2508.00454/) (2025)
- [Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking](https://mlsanity.com/paper/2401.05200/) (2024)
- [Token-Efficient Leverage Learning in Large Language Models](https://mlsanity.com/paper/2404.00914/) (2024)
- [SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models](https://mlsanity.com/paper/2408.02302/) (2024)
- [LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG](https://mlsanity.com/paper/2411.04476/) (2024)
- [OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning](https://mlsanity.com/paper/2405.05957/) (2024)
- [Finetune Once: Decoupling General & Domain Learning with Dynamic Boosted Annealing](https://mlsanity.com/paper/2509.26242/) (2025)
- [CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards](https://mlsanity.com/paper/2606.00020/) (2026)

## Abstract (arXiv)

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.