Search Analytics Sampling for Non-Deterministic AI Outputs
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Solution Overview
Problem
Existing deep learning models rely heavily on large amounts of labeled data, which is time-consuming and costly to annotate, and there is a lack of certainty in predicting language model outputs, making accurate analysis of Generative AI outputs crucial but challenging.
Innovation Solution
An apparatus and method for auditing AI systems by repeatedly testing with similar inputs to ensure a sufficient sample size of non-deterministic outputs, using techniques like Dirichlet Process, Gibbs Sampling, and Generative Adversarial Networks to determine when a representative sample is achieved, thereby reducing resource wastage and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models use larger numbers of parameters to improve description capability, then model performance is improved, but the amount of training data required increases significantly
Solution Approach 1:
The patent applies preliminary action by using synthetic data generation and pre-training methods (such as Transformers, BERT, and GPT) to prepare base data and initialize models before actual training. This allows the model to start with pre-processed knowledge structures, reducing the amount of labeled training data needed for achieving high performance.
Solution Approach 2:
The patent introduces synthetic data as an intermediary between available unlabeled data and the requirements of large-parameter models. By generating synthetic training samples and using them for pre-training, the system bridges the gap between limited real labeled data and the data hunger of large models, enabling effective training with reduced labeled data requirements.
2Measurement precision
If data annotation is performed to improve model performance, then accuracy is improved, but time and labor costs increase significantly
Solution Approach 1:
The patent applies copying by generating synthetic data samples that replicate the characteristics and distribution of real labeled data. These synthetic copies serve as training examples without requiring manual annotation, thereby maintaining model accuracy while eliminating the time-consuming annotation process. The synthetic data captures essential patterns and structures needed for effective model training.
Solution Approach 2:
The patent enables self-service by implementing automated synthetic data generation systems that create training data without human intervention. The system automatically generates labeled training samples, performs pre-training, and iterates on model improvements, reducing dependency on manual data annotation and enabling continuous self-improvement of the model.
3Productivity
If synthetic data is generated to speed up model iteration, then development speed is improved, but data quality for high-precision models may be insufficient
Solution Approach 1:
The patent applies merging by combining synthetic data and real labeled data in a hybrid training approach. Synthetic data provides large-scale coverage and diverse scenarios for rapid iteration, while real labeled data ensures high precision and accuracy. The system merges these data sources strategically, using synthetic data for pre-training and base model development, then fine-tuning with real data to achieve high-precision performance.
Solution Approach 2:
The patent applies dynamics by implementing an adaptive data strategy that dynamically adjusts the mix of synthetic and real data based on model development stage and performance requirements. The system transitions from heavy synthetic data usage in early iterations to increasing real data proportion as the model matures, optimizing both iteration speed and final precision at different stages of development.
4Reliability
If human-in-the-loop methods are used to integrate human knowledge, then model accuracy with sparse data is improved, but system complexity and operational cost increase
Solution Approach 1:
The patent applies taking out by extracting and formalizing human knowledge into structured representations that can be directly integrated into the model training process. Instead of relying on continuous human-in-the-loop operations, the system extracts key knowledge patterns, constraints, and rules from human expertise and embeds them as prior knowledge in the learning framework, reducing the need for ongoing human intervention while maintaining high accuracy with sparse data.
Data Source
AI summary
The technology disclosed relates to a method of agent-generated search analytics. In particular, the technology disclosed relates to inducing an agent-under-test (AUT) to disclose respective outputs in response to processing a target input probe, analyzing the respective outputs and generating one or more analytics corresponding to the target input probe, and causing a topic large language model (LLM) to identify topics by sampling the respective outputs, and storing the topics in memory for further use.