Simultaneous Translation Attention Mask for KV Cache Alignment
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Solution Overview
Problem
Current methods for fine-tuning large language models (LLMs) for simultaneous translation face inefficiencies due to computational overhead from key-value caching and restrictive decision policies, leading to mismatches between fine-tuning and inference, and positional confusion during KV caching.
Innovation Solution
The introduction of an attention mask called SimulMask, which applies a read-write decision policy to mask selected attentions in the attention matrix, ensuring the LLM only attends to available source tokens during inference, and uses bias vectors to adjust attention behavior, enhancing computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current fine-tuning methods use data augmentation or prompt structure modifications, then translation performance may be improved, but computational efficiency deteriorates due to KV caching overhead and expanded training sets
Solution Approach 1:
The patent extracts and removes the harmful KV caching mechanism from the fine-tuning process. By identifying that KV caching causes computational overhead and mismatches between fine-tuning and inference, the invention eliminates this component during fine-tuning while retaining it only during inference, thereby improving computational efficiency without sacrificing translation performance
Solution Approach 2:
The patent introduces dynamic decision policies that adaptively determine when to read source tokens and when to write target tokens during translation. This dynamic approach allows the system to optimize the translation process in real-time, improving both translation quality and computational efficiency by avoiding rigid, pre-defined translation patterns
2Speed
If KV caching is used during inference, then translation speed may improve, but positional confusion occurs during the caching process
Solution Approach 1:
The patent introduces an attention mask as an intermediary component that mediates between the KV caching mechanism and the translation process. The attention mask prevents positional confusion by selectively blocking certain attention connections while allowing others, thereby maintaining both the speed benefits of KV caching and the positional accuracy needed for correct translation
Solution Approach 2:
The patent modifies the attention mechanism by changing the parameters of the attention matrix through masking. By dynamically adjusting which attention weights are active and which are suppressed, the system maintains positional accuracy while still benefiting from the computational efficiency of KV caching, thus resolving the contradiction between speed and precision
3Reliability
If prompt size is increased to improve translation quality, then translation fidelity may improve, but device complexity increases
Solution Approach 1:
The patent segments the translation process into distinct phases: reading source tokens and writing target tokens. By dividing the translation task into these manageable segments controlled by decision policies, the system achieves high translation fidelity without requiring excessively complex or lengthy prompts, thereby reducing device complexity while maintaining quality
Data Source
AI summary
A computer-implemented method for fine-tuning an autoregressive large language model (LLM) for simultaneous translation is disclosed. The method can receive an input vector comprising a plurality of tokens including source tokens representing a source sequence, a prompt, and target tokens representing a target sequence. The method can train the LLM using the input vector based on a self-attention mechanism, including generating an attention matrix comprising attentions derived from the input vector, and applying an attention mask to the attention matrix. Some entries of the attention mask have a mask indicator indicating corresponding attentions are masked, while other entries of the attention mask have a no-mask indicator indicating corresponding attentions are not masked. The training also includes applying biases to attentions in the attention mask corresponding to no-mask indicators in the attention mask. For each row of the attention matrix, the applied biases increase linearly from left to right.