Cross-Lingual Prompt Tuning for Frozen Language Models
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Solution Overview
Problem
Existing natural language processing (NLP) models struggle to perform conversational tasks effectively in less-sourced languages due to the limitations of training datasets, leading to poor performance when translating or communicating across different languages.
Innovation Solution
A method for tuning aligned prompts using a loss objective on masked conversations in multiple languages, combining reconstruction and contrastive losses to adapt prompts for cross-lingual conversational tasks, allowing the model to generate accurate outputs across languages without retraining the large language model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP models are trained using datasets in rich-sourced languages only, then the model performance on high-resource languages is improved, but the ability to translate or communicate in less-sourced languages remains limited
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on large-scale multilingual corpora before fine-tuning on specific task datasets. This pre-training phase establishes cross-lingual representations that enable the model to handle low-resource languages effectively, even before encountering them during task-specific training. The model learns linguistic patterns and semantic relationships across multiple languages in advance, which then transfers to improve performance on less-sourced languages during actual NLP tasks.
2Measurement precision
If large language model parameters are retrained for cross-lingual tasks, then the accuracy on diverse languages is improved, but the computational and memory requirements increase significantly
Solution Approach 1:
The patent extracts and freezes the pre-trained language model parameters, separating them from the task-specific adaptation components. Only the task-specific layers and parameters are trained on cross-lingual datasets, while the base model parameters remain fixed. This extraction approach allows the model to leverage pre-learned cross-lingual representations without the computational burden of retraining the entire model, significantly reducing memory and energy requirements while maintaining cross-lingual accuracy.
Solution Approach 2:
The patent creates a universal base model through pre-training that serves multiple functions across different languages and tasks. This single pre-trained model provides cross-lingual capabilities that can be applied to various NLP tasks without requiring separate training for each language or task combination. The universal representations enable the model to handle diverse languages efficiently, reducing the need for extensive retraining and lowering computational requirements.
3Loss of time
If NLP models are trained with limited datasets for less-sourced languages, then the training time is reduced, but the model performance on those languages deteriorates
Solution Approach 1:
The patent introduces pre-trained cross-lingual representations as an intermediary that bridges the gap between limited task-specific data and the need for robust language understanding. The pre-trained model acts as a mediator that provides rich linguistic knowledge and cross-lingual transfer capabilities, allowing the model to achieve better performance even when trained on smaller datasets for specific languages or tasks. This intermediary knowledge transfer compensates for the limitations of small training datasets.
Data Source
AI summary
Embodiments described herein provide a method of training a language model by tuning a prompt. The method comprises masking tokens of first and second conversational texts which have the same semantic meaning but in different languages (e.g., a translation). The masked texts are input to a language model with a prepended soft prompt. The language model generates respective predicted outputs. A loss objective is computed including a masked language model loss. The prompt is updated based on the computed loss objective via backpropagation while keeping the language model frozen.