Language Model Hyperparameter Determination via Gauss-Newton Hessian
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Solution Overview
Problem
Current methods for interpreting and adjusting machine learning models in language processing, such as calculating influence functions, face challenges with large models due to expensive computations and hyperparameter tuning, particularly with the Linear time Stochastic Second-order Algorithm (LiSSA) being impractical for deep networks.
Innovation Solution
The proposed solution involves replacing the Hessian with a well-behaved Gauss-Newton Hessian and using Proximal Bregman Retraining Functions (PBRF) as a ground truth, along with a stochastic iterative approach like LiSSA, to determine hyperparameters based on spectral properties of the Hessian, specifically the trace and largest eigenvalue, to improve convergence and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Linear time Stochastic Second-order Algorithm (LiSSA) is used to calculate influence functions, then measurement precision of model behavior can be improved, but device complexity and computational cost increase significantly for large models
Solution Approach 1:
The patent extracts and removes the problematic Hessian matrix calculation from the influence function computation. By replacing the Hessian with a well-behaved Gauss-Newton Hessian approximation, the method eliminates the need for expensive second-order derivative computations while maintaining the ability to measure model behavior influence accurately.
Solution Approach 2:
The patent uses a stochastic iterative approach with LiSSA that employs temporary, approximate Hessian representations rather than requiring the full, expensive Hessian matrix. This allows the system to perform influence function calculations using computationally inexpensive approximations that are sufficient for the task, avoiding the need for complex, memory-intensive exact Hessian computations.
2Manufacturing precision
If hyperparameter search is performed to optimize model performance, then manufacturing precision of model training can be improved, but loss of time increases due to expensive hyperparameter tuning
Solution Approach 1:
The patent enables the model to determine its own training hyperparameters by calculating spectral properties (trace and largest eigenvalue) of the Gauss-Newton Hessian from the reference dataset. This self-determination mechanism eliminates the need for external hyperparameter search procedures, allowing the model to automatically optimize its training parameters based on its own structural characteristics.
Solution Approach 2:
The patent changes the approach to hyperparameter determination by using spectral properties of the Hessian matrix as direct indicators for setting training parameters. Instead of searching through hyperparameter spaces, the method derives hyperparameter values from the mathematical properties of the model's own weight matrix, transforming the hyperparameter selection process into a direct computation based on model architecture and data characteristics.
3Ease of manufacture
If the Hessian is replaced with Gauss-Newton Hessian, then ease of manufacture of the algorithm can be improved, but measurement precision may be affected
Solution Approach 1:
The patent introduces the Gauss-Newton Hessian as an intermediary approximation between the exact Hessian and the available computational resources. This intermediary object captures the essential spectral properties needed for influence function calculations while being computationally feasible to compute, serving as a bridge between theoretical exactness and practical implementability.
Data Source
AI summary
There are proposed methods, devices, and computer program products for language processing. In the method, a reference dataset is obtained, the reference dataset comprising a plurality of reference samples, a reference sample in the plurality of reference samples comprising: a reference text string and a reference label corresponding to the reference text string, the reference label indicating a processing result of the language processing. An influence of the reference dataset on a loss is determined, the loss being used for updating a language model associated with the language processing based on the plurality reference samples. A hyperparameter is determined for updating the language model based on the influence of the reference dataset. The language model is updated based on the hyperparameter, the loss, and the plurality of reference samples. Therefore, the language model may be updated in an accurate and effective way.