LLM Query Response System with Dynamic Citation Retrieval
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Solution Overview
Problem
Current Large Language Models (LLMs) are limited in providing direct connections to primary sources and accurate citations of resources used in responses, as they rely on fixed training data and are resource-intensive to update, struggling with queries expressed in natural language and lacking real-time dynamic processing capabilities.
Innovation Solution
A question answering and resource gathering system that integrates traditional search engine capabilities with LLMs, featuring a data collection pipeline for ingesting and organizing data, and a retrieval mechanism that processes user queries to generate natural language responses with direct references to primary sources, enabling real-time processing without the need for re-training the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LLMs use fixed training data to generate responses, then they can provide natural language answers, but they cannot provide direct connections to primary sources or accurate citations
Solution Approach 1:
The patent introduces a retrieval mechanism as an intermediary component between the user query and the LLM. This retrieval mechanism fetches relevant information from a data repository and provides it to the LLM, enabling the system to cite primary sources accurately while maintaining natural language response generation. The intermediary layer resolves the contradiction by decoupling the citation function from the generation function.
2Reliability
If LLMs are re-trained with updated data, then they can provide current information, but the process is extremely resource intensive
Solution Approach 1:
The patent implements a data collection pipeline that pre-processes and organizes data from multiple sources before it is needed for query response. This preliminary action includes ingesting, cleaning, and structuring data in advance, storing it in a searchable repository. When queries are made, the system retrieves pre-processed data rather than re-training the LLM, significantly reducing computational resources while maintaining data recency.
3Loss of information
If traditional search engines are used, then they can provide direct links to sources, but they cannot generate natural language responses with contextual understanding
Solution Approach 1:
The patent merges the strengths of traditional search engines (data retrieval and source linking) with the capabilities of LLMs (natural language understanding and generation). The hybrid system combines a retrieval mechanism for source attribution with an LLM for contextual response generation, creating a unified system that provides both accurate citations and natural language answers without requiring users to learn specialized query syntax.
4Reliability
If LLMs process dynamic resources in real-time, then they can provide up-to-date information, but they cannot identify which resources were used or ensure faithful representation
Solution Approach 1:
The patent implements a feedback mechanism where the retrieval mechanism provides information about the retrieved sources to the LLM, and the system tracks which sources were used in generating responses. This feedback loop enables the system to verify that responses faithfully represent the source material and to provide accurate citations, while the structured feedback process manages the complexity of real-time dynamic resource processing.
Data Source
AI summary
A system and method generate answers to user queries by providing natural language responses containing direct citations to primary sources. The system comprises a data collection pipeline that ingests, processes, and organizes data from multiple sources, and a retrieval mechanism that processes user queries, identifies relevant data, and employs a machine learning model, such as a Large Language Model (LLM), to generate natural language responses based on the retrieved data. The generated responses are augmented with direct references to the primary sources, ensuring accurate attribution and up-to-date information. This system combines the natural language capabilities of LLMs with the direct connections to primary sources provided by traditional search engines, delivering real-time, dynamic processing of resources without incurring high re-training costs.