Dual-LLM Financial Report Validation for Hallucinated Metrics
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Solution Overview
Problem
Large language models (LLMs) are susceptible to generating inaccurate or hallucinated financial metrics in response to queries, leading to potential non-compliance with financial regulations.
Innovation Solution
A retrieval-augmented generation (RAG) process using two LLMs, where a first LLM generates financial report content and a second LLM compares the extracted financial metrics to source metrics to identify and correct hallucinations, ensuring compliance with financial regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single LLM is used to generate financial report content, then the process is simple and fast, but the accuracy and compliance of the generated content deteriorates due to hallucinations
Solution Approach 1:
The patent divides the report generation process into two distinct stages: a first LLM generates the initial report content efficiently, while a second LLM independently validates the financial metrics. This segmentation allows each model to specialize in different tasks, maintaining speed while improving accuracy through dedicated validation.
Solution Approach 2:
The second LLM acts as a feedback mechanism that extracts financial metrics from the generated report and compares them against source data. This feedback loop identifies and corrects hallucinations, ensuring compliance with financial regulations while maintaining the efficiency of the overall process.
2Reliability
If a validation process using a second LLM is added, then the accuracy and compliance of financial report content is improved, but the system complexity increases
Solution Approach 1:
The second LLM performs multiple functions: it extracts financial metrics from the report, compares them against source data, identifies hallucinations, and ensures regulatory compliance. This multi-functionality justifies the addition of a second model by consolidating several validation tasks into a single component.
Solution Approach 2:
The second LLM serves as an intermediary between the generated report and the required compliance standards. It mediates the comparison between report content and source financial metrics, providing a layer of verification that ensures regulatory adherence without requiring direct complex interactions between all system components.
3Measurement precision
If financial metrics are extracted and compared manually, then the validation is thorough, but the time required for processing increases
Solution Approach 1:
The patent replaces manual verification processes with automated LLM-based extraction and comparison. The second LLM automatically extracts financial metrics from the generated report and compares them against source data, eliminating the need for manual review while maintaining thorough detection of hallucinations.
Solution Approach 2:
The validation process operates continuously as part of the report generation workflow rather than as a separate batch process. The second LLM performs extraction and comparison in real-time during the generation process, ensuring that validation is ongoing and does not add significant delays to the overall timeline.
Data Source
AI summary
A method, system, and non-transitory computer-readable medium are disclosed for validating financial report content generated through a retrieval-augmented generation (RAG) process utilizing large language models (LLMs). Source financial metrics are retrieved and provided to a first LLM for generating financial report content. A second LLM extracts financial metrics from the report content and compares them to the source financial metrics to detect hallucinated financial metrics. The second LLM may also evaluate the report content for financial advice or compliance violations.