18587008. GRADIENT CONTROL DEVICE AND GRADIENT CONTROL METHOD OF LANGUAGE MODEL simplified abstract (Kia Corporation)
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 gate tensors, and scaling gradient parts to improve the model's performance.
Key Features and Innovation
- Calculates occurrences of tokens in batch data during training steps
- Groups rare tokens based on occurrence comparison with a threshold value
- Calculates gate tensors on embedding vectors of rare tokens
- Scales gradient parts to optimize the model's performance
Potential Applications
This technology can be applied in natural language processing, machine translation, sentiment analysis, and other text-based AI applications.
Problems Solved
This technology addresses the challenge of effectively training language models by focusing on rare tokens and optimizing their impact on the model's performance.
Benefits
- Improved accuracy and efficiency of language models
- Enhanced performance in handling rare tokens
- Better adaptation to diverse language patterns
Commercial Applications
- Natural language processing software development
- AI-driven content generation tools
- Sentiment analysis platforms for marketing research
Prior Art
Researchers can explore prior studies on gradient control in language models, tokenization techniques, and optimization methods in NLP.
Frequently Updated Research
Stay updated on advancements in gradient control techniques for language models, tokenization algorithms, and optimization strategies in NLP.
Questions about Gradient Control in Language Models
How does this technology improve the training of language models?
This technology enhances the training process by focusing on rare tokens and optimizing their impact on the model's performance, leading to improved accuracy and efficiency.
What are the potential applications of this innovation beyond language models?
This technology can be applied in various text-based AI applications such as machine translation, sentiment analysis, and natural language processing, enhancing their performance and accuracy.
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.
