LLM Source Attribution for Verifiable Multi-Source Responses
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Solution Overview
Problem
Users of generative AI systems, particularly large language models (LLMs), struggle to determine the origin or source of information in responses, leading to uncertainty and decreased trust in these systems.
Innovation Solution
LLMs are configured to generate data queries based on user queries, retrieve information from connected data sets, and include the source of the information within the response, using techniques such as dual-pass processing and clear attribution methods like footnotes or hyperlinks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs process information from multiple sources to generate comprehensive responses, then the quality and completeness of information is improved, but the ability to trace and identify the origin of each piece of information deteriorates
Solution Approach 1:
The patent segments the information processing by maintaining separate tracking of each information source throughout the LLM's processing pipeline. Each piece of information is tagged with its origin identifier, allowing the system to aggregate comprehensive information from multiple sources while preserving the ability to trace each contribution back to its source through structured data associations.
2Loss of information
If LLMs provide detailed responses with multiple information sources, then the completeness of information is improved, but user ability to verify and determine the origin of information deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where source attribution information is continuously tracked and fed back through the processing pipeline. The system monitors information flow from multiple sources, maintains attribution metadata, and feeds this back to generate responses that include verifiable source references, enabling users to easily trace and verify information origins without compromising response completeness.
3Reliability
If LLMs aggregate data from multiple data sets to improve response quality, then the comprehensiveness of information is improved, but the complexity of tracking information origins increases
Solution Approach 1:
The patent creates a universal tracking framework that handles multiple data sets and information sources through a single integrated system. The attribution mechanism is designed to work uniformly across different data sources, using standardized metadata structures and consistent tracking protocols that simplify the complexity of managing information origins across diverse data sets while maintaining comprehensive response quality.
Data Source
AI summary
A large language model (LLM) may receive a query via a user interface of a client device. The LLM may generate one or more data queries from the query to query one or more data sets. The LLM may then transmit the one or more data queries to the one or more data sets. The LLM may then receive information associated with the query and a source for the information from the one or more data sets. The source may be indicative of a location within the one or more data sets from where the information was obtained. Following, the LLM may generate a response to the query that includes the information associated with the query and the source for the information and transmit the response to the user interface of the client device for display.