17947946. NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS simplified abstract (NVIDIA Corporation)
Contents
- 1 NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS - 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 Original Abstract Submitted
NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS
Organization Name
Inventor(s)
Ryan Leary of Woodstock GA (US)
Jonathan Cohen of Mountain View CA (US)
NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17947946 titled 'NATURAL LANGUAGE PROCESSING APPLICATIONS USING LARGE LANGUAGE MODELS
Simplified Explanation
Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.
- Specific types of tasks can be performed using a large model without retraining.
- Custom endpoints can be trained for specific tasks based on guidance mechanisms added to requests.
Potential Applications
This technology could be applied in various fields such as natural language processing, machine learning, and artificial intelligence.
Problems Solved
This technology eliminates the need to retrain models for specific tasks, saving time and resources. It also allows for a single model to perform a variety of tasks.
Benefits
The benefits of this technology include increased efficiency, flexibility in task performance, and cost-effectiveness in model training and deployment.
Potential Commercial Applications
One potential commercial application of this technology could be in chatbots or virtual assistants, where a single model can handle multiple types of user queries efficiently.
Possible Prior Art
Prior art in the field of machine learning and natural language processing may include similar approaches to task performance without retraining models, but this specific method of using guidance mechanisms in requests may be novel.
Unanswered Questions
== How does this technology compare to traditional methods of retraining models for specific tasks? This article does not directly compare the performance or efficiency of this technology with traditional methods of retraining models. Further research or experimentation may be needed to determine the advantages and limitations of this approach.
== What are the potential limitations or challenges of implementing guidance mechanisms in requests for task performance by models? The article does not address any potential limitations or challenges that may arise when implementing guidance mechanisms in requests. It would be important to investigate factors such as scalability, complexity, and compatibility with different models to fully understand the practical implications of this technology.
Original Abstract Submitted
Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.
