Transformer Layer Fine-Tuning for Low-Resource Intent Recognition
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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.
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 to reduce training data and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained language model is fine-tuned using all layers, then model accuracy is improved, but training time and computational resources increase significantly
Solution Approach 1:
The patent segments the model fine-tuning process by dividing layers into two distinct groups: frozen layers (lower layers) and trainable layers (upper layers). This segmentation allows the system to update only a subset of parameters while keeping other parameters fixed, thereby reducing the computational burden and training time while maintaining adequate accuracy for specific downstream tasks.
Solution Approach 2:
The patent applies partial action by updating only the necessary portion of model parameters (those in the upper layers) rather than all parameters. This selective fine-tuning approach updates only the parameters that are most relevant to the specific downstream task, achieving sufficient accuracy without the excessive computational cost of full model fine-tuning.
2Measurement precision
If a pre-trained language model is fine-tuned using all layers, then model accuracy is improved, but computational resources and training data requirements increase
Solution Approach 1:
The patent segments the parameter space into frozen parameters (lower layers) and trainable parameters (upper layers). This segmentation reduces the total number of parameters that require gradient computation and memory storage during backpropagation, thereby reducing computational resource consumption while maintaining adequate entity extraction accuracy.
Solution Approach 2:
The patent updates only the necessary parameters in the upper layers that are most relevant to entity extraction tasks, rather than updating all parameters. This partial update approach reduces computational resource requirements including GPU memory, floating-point operations, and training data throughput needs.
3Adaptability or versatility
If all parameters are updated during fine-tuning, then model adaptability is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the fine-tuning configuration into two distinct sets: frozen layers and trainable layers. This segmentation simplifies the implementation by providing clear configuration options (such as a freeze_parameter_name flag) that guide the optimization process, reducing the complexity of managing all parameters while maintaining adequate task-specific adaptability.
4Measurement precision
If extensive training data is used for fine-tuning, then model performance is improved, but training time and resource requirements increase
Solution Approach 1:
The patent updates only the parameters in the upper layers that are most critical for conversational understanding tasks, rather than updating all parameters. This partial update approach reduces the amount of training data needed to achieve satisfactory performance, as fewer parameters require conditioning on extensive training corpora, thereby improving 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.