LLM Question Answering With Retrieval and Citation Verification
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Solution Overview
Problem
Large language models like GPT-4 can hallucinate and generate incorrect information, which is detrimental in educational contexts, impairing learning.
Innovation Solution
Implementing guardrails and safeguards in generative models to minimize hallucinations, using internal databases for answers and explanations, and leveraging generative models like LLMs for additional coverage, with techniques such as retrieval-augmented generation and citation generation to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for generating answers and explanations, then the coverage and capability of the learning platform are enhanced, but the hallucination rate and accuracy of information increase
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between the LLM and the final output. This system includes multiple layers: retrieval-augmented generation (RAG) that fetches ground truth from internal databases, citation generation that attributes sources, and hallucination detection mechanisms that verify LLM outputs against reliable sources before presentation to users.
Solution Approach 2:
The patent implements feedback loops where the system continuously monitors LLM performance, tracks hallucination patterns, and uses this information to refine verification processes. The citation generation component provides feedback by indicating which statements are supported by evidence, allowing the system to learn from accuracy metrics and improve future answer generation.
2Reliability
If guardrails and safeguards are implemented to minimize hallucinations, then the information accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the answer generation process into distinct modular components: LLM generation, retrieval-augmented verification, citation generation, and hallucination detection. Each module handles a specific aspect of the task, making the overall complex system manageable through clear separation of concerns and independent optimization of each component.
Solution Approach 2:
The system incorporates self-verification capabilities where the LLM-generated answers automatically undergo internal validation through citation generation and cross-checking against internal databases. The system serves itself by detecting and correcting potential hallucinations without requiring external manual verification, reducing the operational burden despite increased structural complexity.
3Reliability
If internal databases are used for answers and explanations, then the accuracy and reliability of information improve, but the coverage and versatility of the platform are limited
Solution Approach 1:
The patent merges two information sources: the reliable but limited internal database and the versatile but potentially inaccurate LLM. The system combines retrieval-augmented generation (fetching from internal databases) with LLM-generated content, creating a hybrid approach that leverages the strengths of both sources to achieve both accuracy and coverage.
Solution Approach 2:
The patent creates a composite answer generation system that combines multiple material (information) types: verified facts from internal databases, contextual understanding from LLMs, and attribution through citations. This composite approach produces answers that are both reliable (grounded in verified information) and versatile (enhanced by LLM capabilities).
Data Source
AI summary
Generative question answering and explanation includes receiving a question. It further includes identifying a type of the question. It further includes constructing a prompt to provide to a generative model based on the identified type of the question. It further includes providing output based on a generative response provided by the generative model. The output further comprises a citation. The citation is based at least in part on one or more contextual passages.