AI Output Anomaly Detection Through Token Source Comparison
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Solution Overview
Problem
Existing AI models often generate inaccurate or anomalous outputs, including hallucinations, which can lead to significant consequences in decision-making processes due to the lack of reliable anomaly detection methods.
Innovation Solution
A system that generates two sets of tokens from AI model outputs and reliable sources for comparison, using text filtering and tokenization to detect anomalies and ensure match accuracy, thereby validating the outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to generate outputs, then productivity and decision-making capability are improved, but reliability and accuracy deteriorate due to hallucinations and erroneous outputs
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between the AI model output and the final decision-making process. This system includes components that retrieve original data, generate expected outputs through rule engines, and compare these with actual AI model outputs to detect anomalies. The intermediary layer validates AI outputs without replacing the AI model, thus maintaining productivity while improving reliability.
Solution Approach 2:
The patent implements a feedback mechanism where the anomaly detection system continuously monitors AI model outputs, compares them against expected values derived from rule engines, and provides feedback for correction. When anomalies are detected, the system generates anomaly indicators that can trigger re-evaluation or correction processes, creating a closed-loop system that improves output reliability over time while maintaining high productivity.
2Reliability
If anomaly detection methods are implemented, then reliability and output accuracy are improved, but device complexity increases due to additional verification systems
Solution Approach 1:
The patent segments the anomaly detection system into distinct modular components: a rule engine that generates expected outputs, a comparison module that identifies anomalies, and an anomaly indicator generator. This segmentation allows each component to perform a specific function independently, making the overall system easier to manage and maintain despite its increased complexity. The modular structure enables selective deployment and optimization of different detection mechanisms.
Solution Approach 2:
The patent applies partial action by implementing anomaly detection selectively rather than uniformly across all AI model outputs. The system can adjust the stringency of detection based on the criticality of specific outputs or contexts, applying more rigorous verification only where necessary. This approach maintains reliability for critical decisions while reducing unnecessary complexity for less critical operations.
3Measurement precision
If token comparison and validation processes are applied, then measurement precision and anomaly detection capability are improved, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing rule engine outputs and expected values before they are needed for comparison. The system prepares validation data in advance, so when AI model outputs require verification, the comparison can be performed quickly against pre-established benchmarks. This reduces the time penalty of validation while maintaining high detection precision.
Solution Approach 2:
The patent enables dynamic adjustment of validation parameters such as anomaly thresholds, confidence levels, and detection sensitivity. By changing these parameters based on contextual requirements, the system can optimize the balance between detection precision and processing time. For time-critical applications, less stringent parameters reduce validation time while maintaining adequate precision; for high-stakes decisions, more stringent parameters enhance precision with acceptable time investment.
Data Source
AI summary
Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.