Transformer Attention Biasing for Gradient-Free LM Adaptation
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Solution Overview
Problem
Existing methods for training language models during inference are costly and complex, requiring gradient descent, which is impractical in production environments, necessitating a need for more efficient training techniques.
Innovation Solution
Implementing bias parameters for transformer-based language models to adjust attention weights during inference without computing gradients, allowing continuous training and refinement based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient descent is used for training language models during inference, then model performance and accuracy are improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent extracts and isolates only the attention layer parameters for updating, rather than updating all model parameters through full gradient descent. This selective extraction of trainable parameters significantly reduces computational complexity while maintaining the ability to improve model accuracy through continuous learning during inference.
Solution Approach 2:
The patent segments the language model into fixed parameters and trainable attention parameters, allowing differential treatment during inference. The segmentation enables efficient updating of only the attention mechanisms while keeping the rest of the model frozen, resolving the contradiction between accuracy improvement and computational cost.
2Adaptability or versatility
If gradient descent is used for training language models during inference, then model adaptability is improved, but computational overhead increases
Solution Approach 1:
The patent extracts only the essential attention parameters for training during inference, eliminating the need for computationally expensive full-model gradient descent. This extraction approach maintains model adaptability to new tasks while significantly reducing energy consumption and computational overhead in production environments.
3Measurement precision
If full model training is performed during inference, then model performance is improved, but training cost increases
Solution Approach 1:
The patent takes out only the attention layer parameters for training during inference, rather than training the entire model. This selective parameter extraction maintains model performance improvement while dramatically reducing training costs and energy loss, making continuous learning feasible in production environments.
Data Source
AI summary
During a first prompt session, a method includes receiving a first prompt specifying a task for a language model (LM). For each biased attention layer of the LM, the method also includes: computing, based on the first prompt, a set of attention weights; and computing bias parameters for biasing a subsequent computation of the set of attention weights during a second prompt session. During the second prompt session, the method also includes receiving a second prompt specifying another task for the LM. For each biased attention layer, the method also includes: computing, based on the second prompt, the set of attention weights; and biasing, using the bias parameters computed during the first prompt session, the set of attention weights. The method also includes generating a corresponding response based on the biased sets of attention weights.