Summary of A Recipe For Building a Compliant Real Estate Chatbot, by Navid Madani et al.
A Recipe For Building a Compliant Real Estate Chatbot
by Navid Madani, Anusha Bagalkotkar, Supriya Anand, Gabriel Arnson, Rohini Srihari, Kenneth Joseph
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a chatbot designed for the real estate domain, focusing on ensuring its behavior is compliant with human preferences to avoid perpetuating discriminatory practices like steering and redlining. The authors build upon previous work by creating a synthetic dataset for general instruction-following and safety data. They fine-tune a llama-3-8B-instruct model through extensive evaluations and benchmarks, achieving significant performance enhancements while making it safer and more compliant. The paper’s contributions include the development of a method for generating the synthetic dataset, the open-sourcing of the model, data, and code to support further research in the community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a chatbot that helps people buy or sell houses without being unfair. They want to make sure it doesn’t treat people differently based on things like race or income. To do this, they made a special dataset with fake instructions and safety rules. Then, they used a model called llama-3-8B-instruct to test the chatbot’s abilities. The result is a safer and more fair chatbot that can help people in the real estate industry. |
Keywords
» Artificial intelligence » Llama
