AI System Testing With Convergence-Based Output Sampling
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Solution Overview
Problem
Existing methods for testing artificial intelligence systems, particularly large language models, face challenges in accurately determining a sufficient sample size for non-deterministic outputs, leading to inefficient resource usage, potential throttling, and inaccurate analysis due to insufficient or excessive querying.
Innovation Solution
An apparatus and method that employs a networked computer system to iteratively test AI systems by generating prompts and topics, using probabilistic methods to determine when a sufficient sample size is achieved, ensuring accurate analytics through adaptive sampling techniques like Monte Carlo simulations and convergence monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative querying of AI systems is performed to obtain sufficient samples for accurate analytics, then measurement precision is improved, but loss of time and energy increases due to excessive querying
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring the distribution of AI outputs and comparing successive samples to determine when convergence is achieved. This feedback loop allows the system to stop querying once sufficient accuracy is obtained, preventing excessive time consumption while maintaining measurement precision.
Solution Approach 2:
The patent applies partial action by querying the AI system only as many times as necessary to achieve sufficient sampling accuracy. Instead of performing a fixed large number of queries, the system performs the minimum necessary queries based on real-time assessment of output distribution convergence, thereby reducing time loss while maintaining adequate measurement precision.
2Measurement precision
If iterative querying of AI systems is performed to obtain sufficient samples for accurate analytics, then measurement precision is improved, but use of energy increases due to excessive querying
Solution Approach 1:
The system uses feedback to monitor output distribution convergence and dynamically adjusts the number of queries performed. By stopping queries once convergence criteria are met, the system avoids unnecessary energy consumption while maintaining accurate measurement of AI outputs.
Solution Approach 2:
The patent implements partial action by performing only the necessary number of queries to achieve sufficient sampling accuracy. The system assesses convergence in real-time and stops querying when adequate samples are obtained, thereby reducing energy usage compared to performing a fixed large number of queries.
3Loss of time
If insufficient querying is performed on AI systems, then loss of time and energy is reduced, but measurement precision deteriorates due to insufficient sampling
Solution Approach 1:
The system implements feedback mechanisms that continuously assess whether sufficient samples have been collected by monitoring output distribution convergence. This ensures that querying stops at the optimal point where adequate measurement precision is achieved without performing unnecessary additional queries that would waste time.
4Reliability
If AI systems are queried multiple times to capture non-deterministic output distribution, then reliability of analysis is improved, but device complexity increases due to need for probabilistic methods
Solution Approach 1:
The patent employs feedback mechanisms that monitor output distribution and automatically determine when sufficient convergence has been achieved. This feedback-driven approach simplifies the testing process by eliminating the need for complex manual determination of sample sizes while maintaining reliable analysis of non-deterministic AI outputs.
Solution Approach 2:
The system performs self-service by automatically assessing convergence and determining when sufficient sampling has been achieved. The AI testing system monitors its own progress and makes decisions about when to stop querying, reducing the need for external complex control mechanisms while ensuring reliable analysis.
Data Source
AI summary
The technology disclosed relates to a system for constructing a probeable output generative space of an agent-under-test (AUT) for a target input probe. An agent sampling logic is configured to induce an agent-under-test (AUT) to disclose a plurality of outputs in response to processing a target input probe. A space construction logic, having access to the plurality of outputs, is configured to construct a probeable output generative space based on the plurality of outputs. A space probing logic, having access to the probeable output generative space, is configured to probe the probeable output generative space for a query, and to make available results of the probing for further analysis.