Samsung electronics co., ltd. (20240104309). SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR - 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 the method handle input variations and complexities in user queries?
- 1.11 What are the potential limitations or drawbacks of using prompt embeddings in this context?
- 1.12 Original Abstract Submitted
SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR
Organization Name
Inventor(s)
Yen-Chang Hsu of Fremont CA (US)
Harshavardhan Kamarthi of Atlanta GA (US)
Yilin Shen of Santa Clara CA (US)
Hongxia Jin of San Jose CA (US)
SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104309 titled 'SYSTEM AND METHOD FOR EFFICIENT LANGUAGE MODEL EDITING USING CONTEXTUAL PROMPT GENERATOR
Simplified Explanation
The method described in the abstract involves enhancing a large language model (LLM) by generating token embeddings and prompt embeddings based on user input, and using these embeddings to make predictions reflecting new or updated information.
- Receiving input for a large language model (LLM) from a user
- Generating token embeddings based on the input
- Generating prompt embeddings using a contextual prompt generator (CPG)
- Providing token embeddings and prompt embeddings to the LLM
- Outputting a prediction based on the embeddings using the LLM
Potential Applications
This technology could be applied in various fields such as natural language processing, artificial intelligence, and machine learning.
Problems Solved
This technology helps in enhancing the capabilities of large language models by incorporating new or updated information through prompt embeddings.
Benefits
The method allows for more accurate and relevant predictions to be made by the LLM, improving its overall performance and usefulness.
Potential Commercial Applications
- Natural language processing software development
- AI-powered chatbots and virtual assistants
Possible Prior Art
One possible prior art could be the use of contextual embeddings in natural language processing tasks to improve model performance.
Unanswered Questions
How does the method handle input variations and complexities in user queries?
The method does not specify how it deals with different types of input or complex user queries.
What are the potential limitations or drawbacks of using prompt embeddings in this context?
The abstract does not mention any potential limitations or drawbacks of using prompt embeddings in enhancing large language models.
Original Abstract Submitted
a method includes receiving an input for a large language model (llm) from a user. the method also includes generating one or more token embeddings based on the input. the method further includes generating one or more prompt embeddings based on the input using a contextual prompt generator (cpg), the one or more prompt embeddings representing new or updated information that is not contained in existing knowledge of the llm. the method also includes providing the one or more token embeddings and the one or more prompt embeddings to the llm. in addition, the method includes outputting a prediction based on the one or more token embeddings and the one or more prompt embeddings using the llm, wherein the prediction reflects the new or updated information represented by the one or more prompt embeddings.
