Bi-Encoder Distillation for Faster Cross-Domain Search
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Solution Overview
Problem
Neural transformer models require extensive computing resources and time for training and deployment, making them impractical for devices with limited resources and suffer from slow inference speeds, which affects the accuracy and practicality of their applications.
Innovation Solution
A distillation system extracts knowledge from a large pre-trained sequence-to-sequence neural transformer model into a bi-encoder, using a teacher model to generate augmented data pairs and train a bi-encoder with shared embeddings for different domains, enabling efficient cross-domain searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large pre-trained sequence-to-sequence neural transformer model is used to achieve high accuracy in cross-domain search, then accuracy is improved, but computational resources and training time increase enormously
Solution Approach 1:
The patent extracts knowledge from the large pre-trained sequence-to-sequence neural transformer model (teacher model) and transfers it to a smaller bi-encoder model (student model). This is achieved through knowledge distillation where the teacher model's learned representations and decision-making patterns are distilled into the student model, allowing the student model to achieve comparable accuracy with significantly reduced computational resources and faster inference speed.
Solution Approach 2:
The patent creates a simplified copy of the teacher model's knowledge structure by training the bi-encoder model to replicate the teacher model's behavior on translation tasks. The student model is trained using augmented data pairs generated by the teacher model, effectively copying the teacher's knowledge in a more efficient format that requires fewer computational resources while maintaining accuracy.
2Measurement precision
If a large pre-trained sequence-to-sequence neural transformer model is deployed to achieve high accuracy, then accuracy is improved, but inference speed decreases
Solution Approach 1:
The patent extracts the essential knowledge and patterns from the large teacher model and concentrates them into a compact bi-encoder student model. This extraction process removes the computational overhead of the large model while preserving the critical decision-making patterns, resulting in a student model that maintains high accuracy but executes inference much faster due to its smaller size and simplified architecture.
Solution Approach 2:
The patent segments the complex sequence-to-sequence translation task into independent encoding and decoding components that can be processed more efficiently. The bi-encoder model separates the encoding of source sequences from the generation of target sequences, allowing for more efficient inference compared to the sequential processing required by traditional sequence-to-sequence models.
3Measurement precision
If a large pre-trained neural transformer model is fine-tuned on true translation pairs to improve accuracy, then accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the bi-encoder student model using augmented data pairs generated by the teacher model before final fine-tuning on true translation pairs. This preliminary training with synthetically augmented data allows the student model to learn robust patterns and representations that accelerate convergence during subsequent fine-tuning, reducing overall training time while achieving high accuracy.
Solution Approach 2:
The patent uses the teacher model as an intermediary to generate augmented training data that bridges the gap between limited true translation pairs and the student model's learning requirements. The teacher model generates additional training examples through data augmentation, providing the student model with more diverse training data without requiring proportional increases in training time or computational resources.
Data Source
AI summary
A distillation system extracts knowledge from a large pre-trained sequence-to-sequence neural transformer model into a smaller bi-encoder. The pre-trained sequence-to-sequence neural transformer model is trained to translate data from a first domain into a second domain on a large corpus. A teacher model is generated from the pre-trained model by fine-tuning the pre-trained neural transformer model on a smaller translation task with true translation pairs. The fine-tuned model is then used to generate augmented data values which are used with the true translation pairs to train the bi-encoder. The bi-encoder is used for perform cross-domain searches.