Industry-Specific Large Language Model Fine-Tuning
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Solution Overview
Problem
Existing large language models struggle to effectively communicate with service providers in specific industries, such as automotive, due to the lack of industry-specific training and fine-tuning, leading to misunderstandings and inaccuracies in responding to user queries.
Innovation Solution
The development of a method and system that utilize industry-specific large language machine learning models, which are periodically and dynamically fine-tuned with industry-specific language databases, to provide accurate and context-specific responses to user queries related to servicing equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general large language models are used without industry-specific fine-tuning, then the system has broad applicability and lower training costs, but the response accuracy and understanding of industry-specific terminology deteriorate
Solution Approach 1:
The system dynamically adapts the large language model through periodic fine-tuning with industry-specific data. The model transitions from a general-purpose state to an industry-specialized state based on operational needs, maintaining both broad applicability and response accuracy through controlled adaptation cycles.
Solution Approach 2:
The model's parameters are adjusted through fine-tuning processes that modify its internal representations to better understand industry-specific terminology and contexts. This parameter optimization enables the model to maintain high response accuracy while retaining its fundamental broad applicability.
2Measurement precision
If industry-specific large language models are periodically and dynamically fine-tuned, then the response accuracy and intent recognition improve, but the training time and computational resources increase
Solution Approach 1:
Instead of continuous fine-tuning, the system employs periodic fine-tuning cycles where the model is retrained at scheduled intervals using industry-specific data. This periodic approach maintains high intent recognition accuracy while significantly reducing the overall time and computational resources required compared to continuous training.
Solution Approach 2:
The system prepares fine-tuning datasets and training configurations in advance, before they are actually needed for model updates. This preliminary preparation reduces the active training time by having all necessary data and parameters ready, thus improving intent recognition accuracy without proportionally increasing training time.
3Device complexity
If general large language models are used, then the system complexity and training data requirements are reduced, but the understanding of industry-specific contexts and terminology deteriorates
Solution Approach 1:
The system pre-processes and curates industry-specific training data before fine-tuning the model. This preliminary data preparation ensures that the model receives high-quality, context-rich training materials that efficiently encode industry-specific knowledge, thereby improving context understanding without proportionally increasing system complexity.
Data Source
AI summary
In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.