18173199. TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY simplified abstract (Adobe Inc.)
Contents
- 1 TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY - 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 Language Model Privacy
- 1.13 Original Abstract Submitted
TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY
Organization Name
Inventor(s)
Franck Dernoncourt of Spokane WA (US)
Thi kim phung Lai of San Jose CA (US)
Rajiv Bhawanji Jain of Falls Church VA (US)
Nikolaos Barmpalios of Sunnyvale CA (US)
Jiuxiang Gu of Baltimore MD (US)
TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18173199 titled 'TRAINING LANGUAGE MODELS AND PRESERVING PRIVACY
Simplified Explanation
A computing device implements a privacy system to predict the next word in a sequence of words by processing input data using a machine learning model trained on training data.
Key Features and Innovation
- Privacy system predicts next word in a sequence of words.
- Machine learning model trained on training data.
- Training data includes sensitive and non-sensitive samples.
- Model parameters updated using subsets of samples associated with clients.
Potential Applications
This technology can be used in:
- Text prediction applications
- Language modeling systems
- Privacy-preserving AI tools
Problems Solved
- Protecting sensitive data in language models
- Improving user privacy in text prediction
- Enhancing data security in machine learning applications
Benefits
- Enhanced privacy protection
- Improved accuracy in word prediction
- Secure handling of sensitive information
Commercial Applications
This technology can be applied in:
- Messaging apps
- Content recommendation systems
- Data analytics platforms
Prior Art
No prior art information available at this time.
Frequently Updated Research
No frequently updated research available at this time.
Questions about Language Model Privacy
Question 1
How does the privacy system protect sensitive data in language models? The privacy system uses subsets of sensitive samples associated with clients to update the machine learning model parameters, ensuring that sensitive information is handled securely.
Question 2
What are the potential applications of this technology beyond text prediction? This technology can also be used in language modeling systems and privacy-preserving AI tools to enhance data security and protect user privacy.
Original Abstract Submitted
In implementations of systems for training language models and preserving privacy, a computing device implements a privacy system to predict a next word after a last word in a sequence of words by processing input data using a machine learning model trained on training data to predict next words after last words in sequences of words. The training data describes a corpus of text associated with clients and including sensitive samples and non-sensitive samples. The machine learning model is trained by sampling a client of the clients and using a subset of the sensitive samples associated with the client and a subset of the non-sensitive samples associated with the client to update parameters of the machine learning model. The privacy system generates an indication of the next word after the last word in the sequence of words for display in a user interface.
