Student–Teacher Language Model Cascades for Secure Query Processing
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Solution Overview
Problem
Cascade systems for neural networks face challenges in balancing performance and data security, particularly when processing sensitive queries, as escalating queries to larger remote models poses data security risks, and conventional methods fail to protect sensitive information while leveraging high performance.
Innovation Solution
A system that processes queries using a student language model neural network and a teacher language model neural network, where the teacher model processes queries characterized to maintain data security, generating example responses that are used by the student model to enhance performance without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If queries are escalated to larger remote models to improve performance, then task performance is improved, but data security deteriorates due to exposure of sensitive information
Solution Approach 1:
The patent introduces an intermediary processing step where the student model generates a teacher query that characterizes the task without including sensitive information from the original input query. This teacher query acts as a mediator that allows the teacher model to provide high-quality responses while preventing exposure of sensitive data. The intermediary transformation ensures that the teacher model receives only necessary task-characterizing information, not the original sensitive input.
2Object-affected harmful factors
If the student model processes all queries independently to maintain data security, then data security is improved, but task performance deteriorates when the student model cannot reliably process complex queries
Solution Approach 1:
The student model serves as an intelligent intermediary that determines when teacher model assistance is needed. It processes the input query and generates a teacher query only when necessary, using heuristics to assess query complexity and sensitivity. This allows the system to maintain data security by default while selectively leveraging the teacher model for complex queries that require additional capability.
Solution Approach 2:
Instead of always escalating to the teacher model or never escalating, the system applies partial action by selectively generating teacher queries only for cases where the student model determines additional help is needed. This avoids excessive use of the teacher model while ensuring adequate performance for complex queries.
3Object-affected harmful factors
If conventional methods like differential privacy are applied to protect sensitive information in queries, then data security is improved, but task performance deteriorates due to obscured information
Solution Approach 1:
The student model extracts only the essential task-characterizing information from the input query while leaving sensitive information behind. This extraction process creates a teacher query that contains sufficient information for the teacher model to generate accurate responses without including the sensitive portions of the original query, thus avoiding the performance degradation associated with differential privacy methods.
Data Source
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AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for performing a task using a teacher language model neural network to provide additional information to a student language model neural network. That is, by receiving an input query, generating an augmented input query using a student language model neural network and a teacher language model neural network, and processing the augmented input query using the student language model neural network to generate a response to the input query for performing the task, the described techniques can both protect the sensitive information in the input query from the teacher language model and leverage the high performance of the teacher language model to generate an accurate response to the input query.