Language Model Output Verification and Citation via Structured Retrieval
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Solution Overview
Problem
Large language models struggle to accurately reproduce specific factual information, particularly in domains like finance, where numerical values and temporal relationships are critical, often hallucinating or approximating data, and fail to surface the most relevant and up-to-date information.
Innovation Solution
Augmenting LLMs with specialized modules for data verification and temporal awareness, enabling them to validate numerical values against ground truth data and provide accurate, temporally aware responses, while converting free-form queries into structured queries like SQL for reliable document retrieval and question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used for document retrieval and question answering, then productivity is improved, but measurement precision deteriorates due to hallucination and approximation of numerical values
Solution Approach 1:
A verification module is introduced as an intermediary component between the LLM and the user. This module independently verifies numerical values and factual claims generated by the LLM against source documents or knowledge bases, acting as a mediator that filters out hallucinations while preserving the LLM's productive capabilities for document retrieval and question answering.
Solution Approach 2:
The system implements feedback mechanisms where the verification module checks LLM outputs and provides corrections when factual errors are detected. This feedback loop allows the system to maintain high productivity while progressively improving measurement precision by correcting hallucinated numerical values and factual information based on verified source data.
2Productivity
If LLMs process unstructured data, then productivity is improved, but reliability deteriorates due to inability to accurately reproduce specific factual information
Solution Approach 1:
The verification module serves as a reliability intermediary that processes the output of the LLM's unstructured data processing. It cross-checks factual claims and numerical values against verified sources, ensuring that the productivity gains from handling unstructured data do not compromise the reliability of extracted factual information.
Solution Approach 2:
The system performs preliminary verification actions before presenting information to the user. By pre-checking factual claims and numerical values against source documents, the system ensures reliability is maintained throughout the unstructured data processing pipeline, preventing the propagation of hallucinated or inaccurate information.
3Ease of operation
If LLMs generate responses without verification, then ease of operation is improved, but measurement precision deteriorates due to hallucination of data
Solution Approach 1:
The verification module operates as an transparent intermediary that does not require user intervention. It automatically verifies numerical values and factual claims in the background, maintaining the ease of operation with simple query interfaces while ensuring measurement precision through automated verification of generated responses.
Data Source
AI summary
A user provides a question to be answered from detailed, dense or otherwise complex documents to a processing system that converts the question to a structured query language query and generates an embedding from the question, augmented by temporal data, synopses, themes, or other relevant information or data. The embedding is compared to embeddings generated from documents of a knowledge base to identify documents that are relevant to the question, and to rank such documents for their relevance. Highly ranking documents are combined with the query and provided to a language model that returns an answer to the question. A source for the answer is identified in at least one of the documents. The answer and the identified documents are presented to the user.