Salesforce, inc. (20240330603). SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Language Model Training
- 1.11 Original Abstract Submitted
SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING
Organization Name
Inventor(s)
Lifu Tu of San Francisco CA (US)
Yingbo Zhou of Palo Alto CA (US)
Caiming Xiong of Menlo Park CA (US)
SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330603 titled 'SYSTEMS AND METHODS FOR CROSS-LINGUAL TRANSFER LEARNING
Simplified Explanation
The patent application describes a method of training a language model by tuning a prompt using masked tokens of conversational texts in different languages with the same meaning.
- Language model training method
- Tuning a prompt using masked tokens of texts in different languages
- Semantic meaning preservation
- Loss objective computation
- Backpropagation for prompt updating
Key Features and Innovation
- Training a language model by tuning a prompt with masked tokens
- Utilizing conversational texts in different languages with the same meaning
- Updating the prompt based on computed loss objective
- Keeping the language model frozen during prompt updating
- Enhancing language model performance through semantic similarity training
Potential Applications
This technology can be applied in:
- Natural language processing
- Machine translation
- Conversational AI systems
- Language model fine-tuning
Problems Solved
- Improving language model performance
- Enhancing semantic understanding in multilingual contexts
- Facilitating cross-language training for language models
Benefits
- Enhanced accuracy in language model predictions
- Improved translation quality
- Better understanding of semantic similarities across languages
- Efficient fine-tuning of language models for multilingual applications
Commercial Applications
- This technology can be utilized in developing advanced language processing systems for various industries such as:
- Translation services
- Chatbots and virtual assistants
- Multilingual content creation platforms
Questions about Language Model Training
A relevant generic question not answered by the article, with a detailed answer
How does tuning a prompt with masked tokens improve the performance of a language model? Tuning a prompt with masked tokens helps the language model learn semantic similarities between different languages, leading to better cross-language understanding and improved accuracy in predictions.
Another relevant generic question, with a detailed answer
What are the potential challenges in training a language model with multilingual data? Training a language model with multilingual data may pose challenges such as data imbalance across languages, language-specific nuances, and ensuring consistent performance across different language pairs. Addressing these challenges requires careful data preprocessing and model fine-tuning strategies.
Original Abstract Submitted
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.
