Trustworthy Generative AI Framework with Modular Output Validation
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Solution Overview
Problem
Existing artificial intelligence systems, particularly large-language models (LLMs), suffer from undesired behaviors such as hallucinations and bias, which can lead to harmful or offensive outputs, and current evaluation methods lack granularity and flexibility in assessing these behaviors.
Innovation Solution
Implementing a trustworthiness estimator with validation models to evaluate LLM outputs for properties like truthfulness and harmfulness, providing metrics and confidence levels, and allowing for modular selection of evaluation metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs are trained to be more cautious to eliminate undesired behaviors, then reliability improves, but productivity deteriorates due to reduced useful output
Solution Approach 1:
The evaluation system is segmented into multiple independent validation models, each specializing in detecting specific properties (truthfulness, harmfulness, readability). This allows selective application of validation to different output types and contexts, maintaining reliability while preserving productivity by not over-validating all outputs uniformly.
Solution Approach 2:
The system dynamically adjusts validation thresholds and confidence levels based on the specific property being evaluated and the context. By changing the parameter of validation stringency rather than applying a fixed cautious approach, the system maintains reliability for critical properties while allowing productive flexibility for less critical cases.
2Measurement precision
If comprehensive validation models are implemented to detect all properties, then measurement precision improves, but device complexity increases
Solution Approach 1:
The validation system is divided into separate modular models for different properties (truthfulness, harmfulness, readability). Each model can be independently developed, trained, and maintained, reducing overall system complexity while achieving comprehensive measurement precision through the combination of specialized models.
Solution Approach 2:
The validation framework provides a universal interface and common infrastructure that supports multiple property evaluations. This multi-functional design allows the system to achieve comprehensive measurement precision without proportionally increasing complexity, as the underlying architecture serves multiple evaluation purposes.
3Measurement precision
If multiple validation metrics are used to evaluate different properties, then measurement precision improves, but loss of time increases due to extended evaluation duration
Solution Approach 1:
The system applies validation metrics selectively based on the specific output and context rather than always applying all metrics. This partial action approach maintains measurement precision for critical properties while reducing evaluation time by skipping less relevant validations, thus resolving the contradiction between comprehensive measurement and time efficiency.
Data Source
AI summary
An embodiment may involve obtaining a prompt for a large-language model (LLM), generating, using the LLM, an output of an artificial intelligence system, obtaining a validation model configured to detect a property in the output, the property indicating a fault in the output, generating, using the validation model on the output, a metric indicating likelihood of the property in the output, determining that the metric satisfies a fault threshold, and in response to determining that the metric satisfies the fault threshold, labeling the output as untrustworthy.