Transformer Layer Fine-Tuning for Low-Resource Language Models
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Solution Overview
Problem
Building intelligent chatbots that understand user intents in natural language is challenging due to the subtleties and ambiguities of language, requiring extensive training data and computational resources, especially for named entity recognition, which is difficult to achieve efficiently.
Innovation Solution
Fine-tuning a pre-trained language model by updating parameters in an incomplete subset of its layers while freezing others, using multi-head attention techniques and caching intermediate values, reduces the need for extensive data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full parameter tuning is performed on all layers of the pre-trained language model, then prediction accuracy is improved, but computational resources and training time increase significantly
Solution Approach 1:
The patent segments the parameter tuning process by dividing the model's layers into two distinct groups: frozen layers (where parameters remain unchanged) and fine-tuned layers (where parameters are updated). This segmentation allows the system to maintain prediction accuracy in frozen layers while reducing computational burden by only actively training parameters in selected fine-tuned layers, thus resolving the contradiction between accuracy and computational resource usage.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of parameters across different layers. Instead of uniformly tuning all parameters, the system selectively applies gradient updates only to specific layers identified as beneficial for the downstream task. This localized parameter tuning maintains necessary accuracy in critical layers while conserving computational resources in less critical layers.
2Measurement precision
If extensive training data is used to train the language model for named entity recognition, then recognition accuracy is improved, but data requirements and training complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on large-scale general language data before fine-tuning for specific named entity recognition tasks. This pre-training phase establishes strong foundational representations that transfer to downstream tasks, reducing the amount of task-specific training data needed while maintaining high recognition accuracy.
Solution Approach 2:
The patent utilizes parameter changes by adjusting only a subset of model parameters during fine-tuning rather than retraining all parameters from scratch. This selective parameter update approach allows the model to adapt to specific named entity recognition tasks with limited training data while preserving the general language understanding acquired during pre-training.
3Adaptability or versatility
If all layers are fine-tuned during training, then model adaptability to specific tasks is improved, but training time and computational cost increase
Solution Approach 1:
The patent applies partial action by fine-tuning only a subset of layers rather than all layers. This selective approach provides sufficient adaptability for many downstream tasks by updating parameters in layers most relevant to the specific task, while avoiding the excessive time and computational cost of training all layers, thus achieving an optimal balance between adaptability and training efficiency.
Data Source
AI summary
Techniques are provided for customizing or fine-tuning a pre-trained version of a machine-learning model that includes multiple layers and is configured to process audio or textual language input. Each of the multiple layers is configured with a plurality of layer-specific pre-trained parameter values corresponding to a plurality of parameters, and each of the multiple layers is configured to implement multi-head attention. An incomplete subset of the multiple layers is identified for which corresponding layer-specific pre-trained parameter values are to be fine-tuned using a client data set. The machine-learning model is fine-tuned using the client data set to generate an updated version of the machine-learning model, where the layer-specific pre-trained parameter values configured for each layer of one of more of the multiple layers not included in the incomplete subset are frozen during the fine-tuning. Use of the updated version of the machine-learning model is facilitated.