Large Language Model Endpoints for Task Customization Without Retraining
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Solution Overview
Problem
Large language models (LLMs) are computationally expensive and impractical for individual users to host and customize for unique use cases, limiting their accessibility and effectiveness in natural language processing tasks.
Innovation Solution
A system architecture that hosts a single LLM in a datacenter, allowing flexible customization through guidance mechanisms like prompt tokens, retrieval set tags, and adaptor weights, enabling multiple users to perform diverse tasks efficiently without retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large language model is trained with additional data for customization, then the model accuracy for specific use cases is improved, but the computational expense and training time increase significantly
Solution Approach 1:
The patent segments the customization process into two distinct phases: (1) training the base LLM once with general domain data, and (2) creating task-specific endpoints with customized prompts and parameters without retraining the entire model. This segmentation allows the computational expensive training to be performed only once, while customization is achieved through lighter-weight configuration changes.
Solution Approach 2:
The patent performs preliminary training of the base LLM with comprehensive domain data before deployment. This preliminary action prepares the model with general knowledge and capabilities, so that subsequent task-specific customizations can be achieved through prompt engineering and parameter adjustment rather than full retraining, significantly reducing computational expense.
2Adaptability or versatility
If multiple customized models are trained for different use cases, then the versatility and adaptability improve, but the device complexity and hosting requirements increase
Solution Approach 1:
The patent creates a universal base LLM that can serve multiple different use cases through a single deployment. By implementing an endpoint customization mechanism that allows different prompts, parameters, and configurations to be applied to the same trained model, the system achieves multi-functionality without requiring multiple separate model instances, thereby reducing hosting complexity.
3Productivity
If a single large language model is hosted centrally, then the productivity and accessibility improve, but the ability to customize for unique use cases deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between the centralized LLM and users, consisting of customizable endpoints. These endpoints act as mediators that translate user-specific task requirements into appropriate prompts and parameters for the centralized model, enabling customization without requiring model replication or direct user access to model weights.
4Measurement precision
If the model size is increased for better performance, then the NLP result accuracy improves, but the ease of operation and accessibility worsen
Solution Approach 1:
The patent creates lightweight copies of task-specific configurations (prompts, parameters, endpoint settings) rather than copying the entire large language model. These configuration copies can be distributed and modified independently by users without requiring them to download or host the massive model weights, making the system accessible while maintaining high accuracy through the centralized LLM.
Data Source
AI summary
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.