LLM Black-Box Representation Using Self-Queries for Model Assessment
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Solution Overview
Problem
Existing methods to understand the behavior of large language models (LLMs) require white-box access to internal states, which is not feasible for closed-source models like GPT4, limiting their explainability and performance assessment.
Innovation Solution
A method to generate black-box representations of LLMs using initialization and elicitation questions, allowing performance evaluation without relying on internal parameters, and using these representations to train linear classifiers for performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If white-box access methods are used to understand LLM behavior, then measurement precision is improved, but adaptability deteriorates because these methods cannot be applied to closed-source models
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the user and the closed-source LLM. This intermediary uses self-query protocols to indirectly probe the model's internal states through carefully constructed questions and analyzes the responses to infer model behavior, enabling measurement without direct white-box access.
Solution Approach 2:
The patent creates a black-box representation that copies or replicates the functional behavior of the LLM without accessing its internal parameters. By querying the model with standardized questions and recording responses, the system creates a surrogate representation that preserves model characteristics while being compatible with closed-source architectures.
2Adaptability or versatility
If black-box representation methods are used, then adaptability is improved by enabling analysis of closed-source models, but measurement precision deteriorates compared to white-box methods
Solution Approach 1:
The patent performs preliminary actions by systematically querying the LLM with initialization and elicitation questions before attempting to understand its behavior. This preliminary probing phase collects response patterns that serve as the basis for subsequent analysis, enabling accurate black-box representation without requiring direct access to internal states.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes the LLM's responses to self-queries and uses this information to refine its understanding of model behavior. The feedback loop involves comparing expected versus actual responses, identifying patterns, and adjusting the representation to improve measurement precision iteratively.
3Measurement precision
If internal parameters are accessed for model representation, then measurement precision is improved, but device complexity increases due to requirements for model access infrastructure
Solution Approach 1:
The patent extracts only the necessary behavioral information from the LLM through targeted self-queries, rather than requiring access to all internal parameters and infrastructure. By taking out only the essential response patterns needed for representation, the system reduces device complexity while maintaining adequate measurement precision.
Data Source
AI summary
Methods for determining black-box representations of machine learning models when information pertaining to internal states or parameters of the models are not accessible are disclosed. By using outputs of the model instead of internal states, the black-box representation is model-agnostic and provides a reliable and robust representation of the model using an external lens. The black-box representation is generated using responses from the model to a series of initialization and elicitation questions that quantify the confidence that the model has in answers it just returned. The black-box representation is then used as a training dataset for a linear classifier in order to learn performance metrics about the model.