LLM Batch Selection with Surrogate Models for Resource-Efficient Training
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Solution Overview
Problem
Batch selection processes for training large-scale machine-learned models are resource-intensive and inefficient, as they require significant computing resources due to the need for forward passes through the entire training data, even for non-selected examples.
Innovation Solution
A surrogate model is used to evaluate training examples and select a subset of training examples with high loss values, which are then used to train a large-scale machine-learned model, reducing the number of examples processed and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch selection is applied to large-scale machine-learned models, then training speed and resource efficiency are improved, but the computing resources required for forward passes through the entire training data increase significantly
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) a forward pass phase where the entire training dataset is processed to compute loss values, and (2) a selective backpropagation phase where only high-loss examples are used for parameter updates. This segmentation allows the system to maintain comprehensive data evaluation while reducing the computational burden of gradient computation, thereby improving training efficiency without sacrificing model quality.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of training examples based on their individual loss values. Instead of uniformly processing all examples, the system identifies and prioritizes high-loss examples for backpropagation. This localized focus ensures that computational resources are concentrated on the most informative samples, optimizing the training process for large-scale models.
2Measurement precision
If the entire training data is processed through forward passes to enable batch selection, then accurate loss computation for example selection is achieved, but computing resources and training time are significantly consumed
Solution Approach 1:
The patent implements preliminary action by performing forward passes through the entire training dataset before the selective backpropagation phase. This preliminary computation of loss values for all examples enables accurate identification of high-loss samples, which are then used for targeted parameter updates. By separating the evaluation phase (forward pass) from the update phase (backpropagation), the system achieves precise loss measurement without requiring time-consuming full processing of all examples during each training iteration.
Data Source
AI summary
A system performs a batch selection process on a small model and uses the results of batch selection to train a large language model (LLM). The system receives training examples and splits the training examples into a holdout set and an evaluation set. Each training example corresponds to a label. The system uses trains a small model using the training examples of the holdout set. The system evaluates the small model on the training examples of the evaluation set, generating a prediction for each training example and computing a loss between the prediction and the training example's label. The system generates an LLM training set by selecting a set of training examples from the evaluation set with the highest loss. The system trains the LLM using the LLM training set.