LLM Response Governance Using Metadata-Tagged Reference Sets
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Solution Overview
Problem
There is no framework for auditing responses generated by large language models (LLM) once they are implemented, leading to potential risks and inaccuracies in applications like intranet chatbots, especially in critical areas such as legal departments, which can result in significant operational issues.
Innovation Solution
A Risk Assessment framework leveraging ORM methodology, using a reference set of input/output pairs tagged with metadata, evaluation criteria, and organizational risk frameworks to evaluate and manage risks associated with LLM responses, generating alerts and historical trend analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional risk management frameworks are used for LLM governance, then organizational risk control is improved, but the ability to accurately measure and evaluate LLM-specific risks (such as hallucinations and perplexity) deteriorates
Solution Approach 1:
The patent creates specialized evaluation criteria tailored to LLM-specific risks (hallucinations, perplexity, bias, drift) while maintaining integration with organizational risk frameworks. This allows different measurement approaches for different types of risks - organizational risks use traditional frameworks while LLM-specific risks use specialized metrics, resolving the contradiction between general risk control and precise LLM measurement
Solution Approach 2:
The evaluation framework is segmented into multiple independent criteria (hallucination detection, perplexity measurement, bias assessment, drift detection) that can be evaluated separately. Each criterion addresses specific LLM risks while collectively providing comprehensive governance, allowing precise measurement of individual risk types without compromising overall organizational risk control
2Measurement precision
If comprehensive evaluation criteria are established for LLM responses, then measurement accuracy is improved, but the complexity of the governance system increases
Solution Approach 1:
The patent creates a universal evaluation framework that handles multiple LLM risks (hallucinations, perplexity, bias, drift) through a single integrated system. The framework uses common infrastructure (reference sets, metadata management, evaluation pipelines) that serves multiple evaluation purposes, reducing overall system complexity while maintaining comprehensive measurement capabilities
Solution Approach 2:
The framework pre-establishes reference sets of inputs and outputs, along with associated metadata and evaluation criteria, before actual LLM evaluation begins. This preliminary preparation creates reusable evaluation assets that simplify ongoing governance operations, reducing the complexity of repeated evaluations while maintaining high measurement precision
3Measurement precision
If reference sets with metadata are created for evaluation, then evaluation accuracy is improved, but the time and resources required for setup increase
Solution Approach 1:
The reference sets and metadata structures are designed to be universal and reusable across multiple evaluation scenarios and LLM instances. Once created, the same reference sets can evaluate different models and different risk types without requiring recreation, amortizing the initial setup time investment across numerous evaluations and reducing the effective time cost per evaluation
Data Source
AI summary
An approach for governing responses generated by a language learning model (LLM) model. The approach defines a reference set of inputs and output pairs for the LLM wherein the reference set of inputs and output pairs are actual inputs and reference outputs. The approach defines a set of metadata associated with the reference set of input and output pairs and assigns the metadata to each pair of the reference set of inputs and output pairs. The approach also defines a set of evaluation criteria, assigns the evaluation criteria to organizational risk framework and associates the set of metadata to the evaluation criteria.