Large Language Model Guidance for Custom NLP Tasks Without Retraining
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Solution Overview
Problem
Large language models (LLMs) are computationally expensive and impractical for many users due to their size and computational requirements, limiting their accessibility and scalability for various unique use cases without the feasibility of training and hosting multiple models for each operation.
Innovation Solution
A system architecture that hosts LLMs in a datacenter, allowing flexible customization for different use cases by using guidance mechanisms such as prompt tokens, retrieval set tags, and adaptor weights, enabling a single LLM to perform diverse tasks efficiently by associating requests with customized endpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large language model is used to provide highly accurate NLP results, then the quality and accuracy of language processing is improved, but the computational cost and resource requirements increase significantly
Solution Approach 1:
The patent segments the monolithic LLM into multiple specialized smaller models, each trained for specific NLP tasks (e.g., language generation, grammar checking, summarization). This segmentation reduces the computational burden of each individual model while collectively maintaining high NLP accuracy across diverse tasks.
Solution Approach 2:
The patent creates a universal NLP system where multiple specialized models work together to handle various language processing tasks. Each model is optimized for specific functions but the collective system provides universal NLP capabilities, reducing the need for a single large model to handle all tasks inefficiently.
2Productivity
If a single large language model is used to serve multiple unique use cases, then resource efficiency is improved, but the ability to customize for specific operations is reduced
Solution Approach 1:
The patent segments the system into multiple specialized models that can be selectively deployed based on specific use cases. Each model is optimized for particular operations (e.g., one for grammar checking, another for summarization), allowing customization while maintaining resource efficiency through selective deployment rather than running a single large model for all tasks.
Solution Approach 2:
The patent implements a dynamic model selection mechanism that adapts which specialized model to use based on the specific NLP task at hand. This dynamic adaptation allows the system to customize its behavior for different operations while maintaining overall resource efficiency by only activating the necessary model for each task.
3Adaptability or versatility
If multiple specialized language models are trained for different use cases, then customization for specific operations is improved, but the complexity of training and hosting multiple models increases
Solution Approach 1:
The patent introduces an intermediary system that manages the deployment and coordination of multiple specialized models. This intermediary handles model selection, load balancing, and task routing, reducing the complexity of managing multiple models by providing a unified interface and automated decision-making layer between the tasks and the specialized models.
4Ease of operation
If a large language model is hosted by a cloud provider for multiple users, then accessibility is improved, but the computational expense and scalability for unique user needs worsen
Solution Approach 1:
The patent segments the cloud-based NLP service into multiple specialized models that can be independently deployed and scaled. This allows the cloud provider to offer customized NLP capabilities to different users based on their specific needs without requiring every user to access a full large language model, improving both accessibility and scalability for unique requirements.
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.