Guidance-Driven Large Language Models for Custom NLP Tasks
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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, making it difficult to train and host multiple models for unique use cases, leading to sub-optimal cloud-based implementations and a lack of customization.
Innovation Solution
A system architecture that hosts a single LLM in a datacenter, allowing flexible customization for various use cases by using guidance mechanisms like prompt tokens, retrieval set tags, and adaptor weights, enabling efficient processing of diverse tasks without retraining the model.
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 large language model into multiple smaller models, each trained on a specific subset of the training data for a particular use case. This segmentation allows each smaller model to process specific tasks with reduced computational overhead while maintaining the accuracy benefits of the original large model for its designated domain.
Solution Approach 2:
The patent creates a universal system where a single large language model can serve multiple different use cases by generating and managing multiple specialized smaller models. This multi-functionality allows one universal infrastructure to handle diverse NLP tasks including but not limited to summarization, grammar checking, and language generation, thereby amortizing the computational cost across multiple functions.
2Adaptability or versatility
If multiple customized large language models are trained for unique use cases, then the adaptability and customization for specific operations is improved, but the device complexity and infrastructure requirements increase
Solution Approach 1:
The patent merges the functionality of multiple customized models into a single unified system. The large language model serves as a central hub that generates smaller specialized models for different use cases, combining the adaptability of customized models with the simplicity of a single centralized infrastructure, thereby reducing device complexity while maintaining customization capability.
Solution Approach 2:
The patent introduces an intermediary mechanism where the large language model acts as a mediator between the training data and the specialized smaller models. This intermediary generates the smaller models on-demand or in advance, managing the complexity of having multiple customized models through a single coordinating system that handles model creation, storage, and deployment.
3Loss of substance
If a single large language model is hosted in a datacenter for multiple users, then the loss of substance and resource waste is reduced, but the adaptability for unique use cases deteriorates
Solution Approach 1:
The patent applies local quality by creating smaller models with specialized characteristics tailored to specific use cases from the general training data. Each smaller model has localized expertise in its domain (e.g., summarization, grammar) while the overall system maintains resource efficiency by sharing the underlying infrastructure and data storage of the single large language model host.
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.