Domain-Tuned LLMs for Device and Network Task Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current large language models (LLMs) are not designed to address specific domain problems in cellular networks, requiring time-consuming and costly fine-tuning, and fail to personalize context for users, leading to inefficient resource consumption and incorrect network solutions.
Innovation Solution
A management system that integrates user-type and task-specific LLMs, utilizing a user interaction log library and fine-tuning process to enhance autonomy and personalization, optimizing task performance and conserving resources by continuously updating LLMs based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If base LLMs are trained from scratch with domain knowledge, then domain-specific accuracy is improved, but time and computational resources are significantly increased
Solution Approach 1:
The patent applies preliminary action by pre-training LLMs on general knowledge and then using efficient fine-tuning techniques to adapt them to specific network management domains. The system prepares and curates domain-specific datasets beforehand, allowing rapid fine-tuning without retraining from scratch, thus reducing training time while maintaining domain accuracy.
Solution Approach 2:
The patent utilizes parameter changes by adjusting model parameters through fine-tuning processes, where only specific parameters are updated based on domain data rather than retraining the entire model. This selective parameter updates approach maintains computational efficiency while achieving domain-specific accuracy.
2Reliability
If base LLMs are fine-tuned with domain knowledge, then domain-specific capability is improved, but computational resources and cost are increased
Solution Approach 1:
The patent applies partial action by performing fine-tuning rather than complete retraining, updating only the necessary parameters of the LLM based on domain-specific data. This partial fine-tuning approach achieves domain capability improvement while significantly reducing computational resource consumption compared to training from scratch.
Solution Approach 2:
The system performs preliminary curating and processing of domain knowledge into structured datasets, which enables efficient fine-tuning with reduced computational overhead. By preparing training data in advance in an optimized format, the system minimizes the computational resources required during the fine-tuning process.
3Ease of operation
If LLMs are trained to provide personalized responses, then user experience is improved, but system complexity and data processing requirements are increased
Solution Approach 1:
The patent segments the LLM system into multiple specialized models, each fine-tuned for specific user types or network management tasks. This segmentation allows personalized responses for different user groups while maintaining manageable system complexity through modular architecture, where each segment can be independently trained and optimized.
Solution Approach 2:
The patent implements local quality by creating user-type-specific LLMs with tailored knowledge and response styles optimized for particular audiences. Each local model variant is fine-tuned on domain knowledge relevant to specific user groups, providing personalized experiences without requiring complete system redesign, thus balancing customization with complexity management.
4Productivity
If continuous fine-tuning is performed to improve task performance, then productivity is improved, but resource consumption and processing time are increased
Solution Approach 1:
The patent implements feedback mechanisms where system performance data and user interactions are continuously collected and used to refine and update LLM models. This feedback loop enables incremental improvements in task performance through continuous learning, allowing the system to adapt to changing network conditions and user needs while optimizing resource consumption through targeted updates based on actual performance metrics.
Data Source
AI summary
A device may receive a query, and may generate an embedding based on the query. The device may determine context for the query based on the embedding and historical data, and may select, based on the context, a base LLM from a plurality of base LLMs. The device may fine-tune the base LLM with LLM fine-tuning data to generate a fine-tuned LLM, and may process the embedding, with the fine-tuned LLM, to identify tasks associated with the query. The device may determine recommended tasks based on the tasks, and may select a task LLM from a plurality of task LLMs. The device may process the tasks and the recommended tasks, with the task LLM, to determine final tasks, and may cause the final tasks to be executed. The device may perform one or more actions based on the final tasks.