Machine-Learning Model Characterization Using Challenge-Query Density
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Solution Overview
Problem
Determining the relationship between outputs from a large language model (LLM) at test time and the specific pieces of training data that contributed to the output is challenging due to the vast amount of web-scale training data.
Innovation Solution
A system and method for characterizing training data in machine-learning models using kernel density estimation (KDE) to determine neighborhood density for challenge queries, allowing for the assessment of training data sufficiency and potential supplementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If web-scale training data is used to train large language models, then model performance and capability are improved, but it becomes difficult to determine the relationship between model outputs and specific training data contributions
Solution Approach 1:
The patent segments the vast training data into manageable units by creating embeddings for individual training samples and organizing them in a vector database. This allows the system to break down the complex relationship between model outputs and training data into discrete, queryable units that can be analyzed systematically through nearest neighbor searches.
2Measurement precision
If kernel density estimation is performed on the entire training dataset, then measurement precision of data distribution is improved, but computational complexity and time increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-computing embeddings for all training data samples before the actual density estimation process. These pre-computed embeddings are stored in a vector database, allowing the KDE algorithm to operate on ready-to-use representations rather than raw data, significantly reducing computational time during the actual analysis phase.
Solution Approach 2:
The system computes embeddings for a subset of training data that is sufficient to achieve accurate density estimation without processing the entire web-scale dataset. By selecting a representative subset for embedding and density calculation, the system achieves the necessary measurement precision while avoiding the prohibitive computational cost of processing all training samples.
3Measurement precision
If comprehensive training data characterization is performed, then identification of data distribution gaps is improved, but computational resources and complexity increase
Solution Approach 1:
The patent introduces embedding vectors as an intermediary representation between raw training data and the density estimation process. These embeddings serve as a mediator that transforms high-dimensional raw data into a compressed, meaningful space where density calculations can be performed efficiently. The embedding layer acts as an intermediary that simplifies the complexity of analyzing vast training datasets.
Data Source
AI summary
Disclosed herein are systems and methods for objectively characterizing machine-learning models including receiving first training data formatted to be used in the training of a machine-learning model; receiving one or more challenge queries formatted to be run on the machine-learning model; generating, for the first training data, a plurality of associated training vectors that embed at least some of the first training data into a vector space; generating, for each of the one or more challenge queries, a plurality of associated challenge vectors that embed at least some of the challenge queries into the vector space; and determining, for each challenge query, a corresponding quality metric for the machine-learning model by determining a neighborhood density for each of the challenge queries in the vector space.