Salesforce, inc. (20240330409). PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING simplified abstract
Contents
- 1 PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Transformer Model Pre-training
- 1.13 Original Abstract Submitted
PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING
Organization Name
Inventor(s)
Chen Xing of Palo Alto CA (US)
Wenhao Liu of Redwood City CA (US)
Chu Hong Hoi of Singapore (SG)
Nitish Shirish Keskar of San Francisco CA (US)
Caiming Xiong of Menlo Park CA (US)
PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330409 titled 'PARAMETER UTILIZATION FOR LANGUAGE PRE-TRAINING
Simplified Explanation
The patent application describes a method for pre-training a transformer model with more parameters for sophisticated patterns (PSP++). The model is divided into a held-out model and a main model, with forward and backward passes performed on each to update parameters based on self-attention hidden states and loss.
- The transformer model is pre-trained using more parameters for sophisticated patterns.
- The model is divided into a held-out model and a main model.
- Forward and backward passes are performed on each model to update parameters based on self-attention hidden states and loss.
Key Features and Innovation
- Pre-training a transformer model with more parameters for sophisticated patterns.
- Division of the model into a held-out model and a main model.
- Utilizing forward and backward passes to update parameters based on self-attention hidden states and loss.
Potential Applications
This technology can be applied in natural language processing, machine translation, and other tasks that require sophisticated pattern recognition.
Problems Solved
- Enhancing the performance of transformer models by pre-training with more parameters.
- Improving the ability of models to recognize complex patterns.
Benefits
- Increased accuracy and efficiency in tasks requiring sophisticated pattern recognition.
- Enhanced performance of transformer models in various applications.
Commercial Applications
Title: Enhanced Transformer Model Pre-training for Advanced Pattern Recognition This technology can be utilized in industries such as AI, data analysis, and automation to improve the accuracy and efficiency of pattern recognition tasks.
Prior Art
Further research can be conducted in the field of transformer model pre-training and parameter optimization to explore existing methods and technologies.
Frequently Updated Research
Stay updated on advancements in transformer model pre-training techniques and parameter optimization to enhance the performance of AI models.
Questions about Transformer Model Pre-training
How does pre-training a transformer model with more parameters improve pattern recognition?
Pre-training with more parameters allows the model to capture sophisticated patterns and nuances in data, leading to enhanced performance in tasks requiring pattern recognition.
What are the potential drawbacks of using more parameters in pre-training a transformer model?
Using more parameters may increase computational complexity and training time, requiring efficient optimization techniques to mitigate these challenges.
Original Abstract Submitted
embodiments are directed to pre-training a transformer model using more parameters for sophisticated patterns (psp++). the transformer model is divided into a held-out model and a main model. a forward pass and a backward pass are performed on the held-out model, where the forward pass determines self-attention hidden states of the held-out model and the backward pass determines loss of the held-out model. a forward pass on the main model is performed to determine a self-attention hidden states of the main model. the self-attention hidden states of the main model are concatenated with the self-attention hidden states of the held-out model. a backward pass is performed on the main model to determine a loss of the main model. the parameters of the held-out model are updated to reflect the loss of the held-out model and parameters of the main model are updated to reflect the loss of the main model.
