@inproceedings{madani-etal-2025-recipe,
title = "A Recipe For Building a Compliant Real Estate Chatbot",
author = "Madani, Navid and
Bagalkotkar, Anusha and
Anand, Supriya and
Arnson, Gabriel and
Srihari, Rohini K. and
Joseph, Kenneth",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.18/",
pages = "213--235",
abstract = "In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure it can be used without perpetuating discriminatory practices like steering and redlining, which have historically plagued the real estate industry in the United States. Building on prior work, we present a method for generating a synthetic general instruction-following dataset, along with safety data. Through extensive evaluations and benchmarks, we fine-tuned a llama-3-8B-instruct model and demonstrated that we can enhance it`s performance significantly to match huge closed-source models like GPT-4o while making it safer and more compliant. We open-source the model, data and code to support further development and research in the community"
}
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%0 Conference Proceedings
%T A Recipe For Building a Compliant Real Estate Chatbot
%A Madani, Navid
%A Bagalkotkar, Anusha
%A Anand, Supriya
%A Arnson, Gabriel
%A Srihari, Rohini K.
%A Joseph, Kenneth
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F madani-etal-2025-recipe
%X In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure it can be used without perpetuating discriminatory practices like steering and redlining, which have historically plagued the real estate industry in the United States. Building on prior work, we present a method for generating a synthetic general instruction-following dataset, along with safety data. Through extensive evaluations and benchmarks, we fine-tuned a llama-3-8B-instruct model and demonstrated that we can enhance it‘s performance significantly to match huge closed-source models like GPT-4o while making it safer and more compliant. We open-source the model, data and code to support further development and research in the community
%U https://aclanthology.org/2025.coling-industry.18/
%P 213-235
Markdown (Informal)
[A Recipe For Building a Compliant Real Estate Chatbot](https://aclanthology.org/2025.coling-industry.18/) (Madani et al., COLING 2025)
ACL
- Navid Madani, Anusha Bagalkotkar, Supriya Anand, Gabriel Arnson, Rohini K. Srihari, and Kenneth Joseph. 2025. A Recipe For Building a Compliant Real Estate Chatbot. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 213–235, Abu Dhabi, UAE. Association for Computational Linguistics.