18587008. GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL simplified abstract (Hyundai Motor Company)
Contents
- 1 GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL - 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 Gradient Control in Language Models
- 1.13 Original Abstract Submitted
GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL
Organization Name
Inventor(s)
GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18587008 titled 'GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL
Simplified Explanation
The patent application describes a device and method for controlling gradients in a language model. This involves identifying rare tokens, calculating a gate tensor, and scaling gradients to improve training efficiency.
Key Features and Innovation
- Device and method for gradient control in a language model
- Identification and grouping of rare tokens
- Calculation of gate tensor on embedding vectors
- Scaling of gradients to optimize training process
Potential Applications
This technology can be applied in natural language processing, machine translation, sentiment analysis, and other text-based AI applications.
Problems Solved
- Efficient handling of rare tokens in language models
- Improved training process for better model performance
- Enhanced accuracy in text analysis tasks
Benefits
- Enhanced model performance
- Improved training efficiency
- Better handling of rare tokens in language processing tasks
Commercial Applications
- Natural language processing software
- AI-powered translation tools
- Sentiment analysis platforms
- Text classification systems
Prior Art
Readers interested in prior art related to this technology can explore research papers on gradient control in language models, rare token handling in NLP, and optimization techniques for text-based AI models.
Frequently Updated Research
Stay updated on the latest advancements in gradient control techniques for language models, rare token handling strategies, and optimization methods for text-based AI applications.
Questions about Gradient Control in Language Models
How does this technology improve the efficiency of training language models?
This technology improves training efficiency by identifying and handling rare tokens effectively, leading to better model performance.
What are the potential applications of this gradient control method in real-world scenarios?
This gradient control method can be applied in various text-based AI applications such as natural language processing, machine translation, and sentiment analysis.
Original Abstract Submitted
Provided are a gradient control device and a gradient control method of a language model. The gradient control device of a language model may include: one or more processors, and memory storing instructions. The instructions, when executed by the one or more processors, may cause the gradient control device to calculate a number of occurrences of each token, of a plurality of tokens, in batch data at each training step of a plurality of training steps ranging from a current training step to a set previous training step; group rare tokens based on a comparison of the calculated number of occurrences of each token, of the plurality of tokens, with a threshold value; calculate a gate tensor on embedding vectors of the grouped rare tokens; and scale a gradient part that pushes the embedding vectors of the grouped rare tokens away from feature vectors having relatively non-rare and feature vectors having relatively rare target tokens, among gradients of a loss function for the embedding vectors of the grouped rare tokens in a training step.
