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 real-time translation.
Innovation Solution
The introduction of an attention mask called SimulMask, which applies a read-write decision policy to autoregressive LLMs, masking attentions based on the number of source tokens needed before generating target tokens, and using bias vectors to adjust attention behavior, ensuring efficient KV caching and alignment with real-time translation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current fine-tuning methods use data augmentation or prompt structure modifications, then translation performance can 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 the fine-tuning phase while maintaining it only during inference, thereby resolving the contradiction between translation performance and computational efficiency
Solution Approach 2:
The patent inverts the conventional approach by applying attention masks during fine-tuning that are opposite to standard causal masking. Instead of masking future tokens, the invention masks past and present tokens for target sequence positions, allowing the model to learn simultaneous translation patterns where target tokens can attend to relevant source tokens regardless of position, thus improving translation performance without excessive computational cost
2Reliability
If prompt sizes are expanded for better translation coverage, then translation quality improves, but computational overhead increases
Solution Approach 1:
The patent applies local quality by implementing position-specific attention masks that treat different positions in the sequence differently. Source tokens retain standard causal masking while target tokens receive specialized masking that allows attention to relevant source positions. This localized differentiation improves translation quality for specific positions without uniformly expanding the entire prompt structure, thereby avoiding proportional increases in computational overhead
3Ease of operation
If a single decision policy is used for simultaneous translation, then implementation simplicity is maintained, but adaptability to different translation scenarios is restricted
Solution Approach 1:
The patent implements dynamics by making the attention mask configuration adaptable to different decision policies. The system allows dynamic adjustment of the read-write decision policy parameter, which controls how many source tokens can be attended to when generating each target token. This dynamic configurability enables the same base model to adapt to different translation scenarios (e.g., more aggressive vs. more conservative translation strategies) without retraining, while maintaining simple implementation through a unified masking framework
4Reliability
If fine-tuning allows attention to all tokens, then model learns comprehensive patterns, but positional confusion occurs during real-time translation
Solution Approach 1:
The patent applies segmentation by dividing the attention mask into distinct segments: source sequence positions and target sequence positions. Each segment receives different masking treatment - source tokens use standard causal masking to prevent looking ahead, while target tokens use specialized masking that selectively allows attention to relevant source tokens. This segmentation enables the model to learn comprehensive translation patterns while maintaining positional accuracy by preventing inappropriate cross-attention that would cause positional confusion
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 one or more source tokens, a prompt, and one or more target tokens. The source tokens represent a source sequence, and the target tokens represent a target sequence translated from the source sequence. The method can train the LLM using the input vector based on a self-attention mechanism, including obtaining a queries and keys respectively corresponding to the tokens in the input vector, generating an attention matrix comprising attentions calculated as dot products of the queries and the keys, and applying an attention mask to the attention matrix. The attention mask can mask selected attentions from the attention matrix based on a read-write decision policy. Related systems and software are also disclosed.