Housing Chatbot Retrieval With Source-Cited LLM Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search engines and information retrieval systems face challenges in understanding natural language requests, providing accurate and specific housing-related information, and adhering to fair housing rules, while efficiently managing user queries.
Innovation Solution
A chatbot system utilizing a trained large language model (LLM) with a query filter and prompt generator to generate responsive housing-related information, incorporating a fair housing rule filter, authoritative sources, and vector-based embeddings to ensure accurate and relevant responses, while maintaining context across user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a chatbot uses a trained large language model to respond to free-form natural language requests, then the ability to understand and respond to user queries is improved, but the accuracy and reliability of the information provided deteriorates due to potential hallucinations and lack of source verification
Solution Approach 1:
The patent introduces an intermediary retrieval system that acts as a mediator between the user's natural language query and the LLM. The system first retrieves relevant authoritative documents using vector-based embeddings, then feeds these retrieved documents to the LLM as context. This intermediary step ensures the LLM bases its responses on verified sources rather than generating potentially inaccurate information from training data alone.
Solution Approach 2:
The system performs preliminary retrieval of relevant information from authoritative sources before the LLM generates its response. By pre-fetching and verifying the necessary information through document retrieval and embedding matching, the system ensures accurate context is available to the LLM before it formulates its answer, preventing hallucinations.
2Adaptability or versatility
If the chatbot provides unrestricted responses to any user query, then the versatility and adaptability are improved, but the system fails to adhere to fair housing rules and legal compliance deteriorates
Solution Approach 1:
The patent implements preliminary anti-action by incorporating a compliance filter that checks user queries against fair housing rules and legal requirements before the LLM generates a response. The system proactively identifies and blocks queries that violate fair housing regulations, preventing potential harmful outputs before they are generated. This aligns with the technical means of using a trained classifier model to identify and reject queries violating fair housing rules.
3Measurement precision
If the system retrieves and processes extensive authoritative documents to ensure accurate information, then the measurement precision and information reliability are improved, but the processing time and complexity increase
Solution Approach 1:
The system performs preliminary processing of authoritative documents by creating vector-based embeddings in advance and storing them in a database. When a user queries, the system quickly retrieves relevant documents by comparing query embeddings against the pre-processed document embeddings, rather than processing full texts in real-time. This preliminary action significantly reduces response time while maintaining high retrieval accuracy.
Solution Approach 2:
The patent replaces traditional text-based information retrieval mechanics with vector-based embedding comparison. Instead of searching through full document texts, the system converts queries and documents into vector representations and uses mathematical similarity calculations to identify relevant information. This substitution of mechanical text processing with mathematical vector operations dramatically improves processing speed while maintaining precision.
4Adaptability or versatility
If the chatbot maintains context across multiple user interactions, then the adaptability and personalized service are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent implements a universal context management mechanism that handles multiple functions within a single architectural framework. The system maintains user context, manages conversation history, and tracks interaction patterns using a unified approach that leverages the LLM's inherent capabilities for context retention. This multi-functional context system serves multiple purposes: personalization, query disambiguation, and conversation coherence, reducing overall system complexity.
Data Source
AI summary
Techniques are described for performing automated operations related to generating and providing housing-related information, such as to automatically respond to free-form natural language query requests for housing-related information of various types received by a chatbot by using a combination of automated tools to generate and provide responsive housing-related information. In at least some situations, the described techniques generates responses using a trained large language model that maintains context over an interaction session with a user involving multiple user queries and corresponding responses, and ensuring accurate response information by restricting the generation of the response information in particular ways and by identifying and providing citations to authoritative sources used to generate the response information.