Huawei technologies co., ltd. (20240127000). METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS simplified abstract
Contents
- 1 METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this method compare to traditional model training techniques?
- 1.11 What are the specific industries or sectors that could benefit most from this technology?
- 1.12 Original Abstract Submitted
METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS
Organization Name
Inventor(s)
Lifeng Shang of Hong Kong (CN)
METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127000 titled 'METHOD AND SYSTEM FOR TRAINING LARGE-SCALE LANGUAGE MODELS
Simplified Explanation
The abstract describes a computer-implemented method for model training, involving determining weights, forming matrices, initializing a target model, and training the model.
- The method involves determining a set of weights based on matrices associated with source and target models.
- The target model is initialized based on the weights and matrices.
- The target model is then trained using the initialized parameters.
Potential Applications
This technology could be applied in various fields such as machine learning, artificial intelligence, and data analysis for model training and optimization.
Problems Solved
This technology helps in improving the efficiency and accuracy of model training by utilizing weights and matrices to initialize and train the target model effectively.
Benefits
The benefits of this technology include faster model training, improved model performance, and enhanced predictive capabilities in various applications.
Potential Commercial Applications
One potential commercial application of this technology could be in developing advanced predictive models for industries such as finance, healthcare, and marketing.
Possible Prior Art
Prior art in this field may include existing methods for model training and optimization using weights and matrices in machine learning and artificial intelligence research.
Unanswered Questions
How does this method compare to traditional model training techniques?
The article does not provide a direct comparison to traditional model training techniques using weights and matrices. It would be helpful to understand the specific advantages or differences of this method compared to traditional approaches.
What are the specific industries or sectors that could benefit most from this technology?
The article does not specify the industries or sectors that could benefit most from this technology. It would be valuable to explore the potential applications and impact of this method in different fields.
Original Abstract Submitted
a computer-implemented method is provided for model training performed by a processing system. the method comprises determining a set of first weights based on a first matrix associated with a source model, determining a set of second weights based on the set of first weights, forming a second matrix associated with a target model based on the set of first weights and the set of second weights, initializing the target model based on the second matrix, and training the target model.
