Domain-Specific LLM Fine-Tuning With Reasoning Distillation
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Solution Overview
Problem
Conventional systems face challenges in efficiently training large language models to perform multiple domain-specific tasks without causing catastrophic forgetting, leading to reduced performance in previously learned tasks.
Innovation Solution
The use of reasoning distillation to fine-tune a single machine learning model for multiple domain-specific tasks, where the model develops statistical correlations by evaluating intermediate steps, thereby mitigating catastrophic forgetting and enhancing generalization across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single large language model is fine-tuned for multiple domain-specific tasks, then the model can perform multiple tasks with high accuracy, but catastrophic forgetting occurs and performance on previously learned tasks deteriorates
Solution Approach 1:
The patent segments the fine-tuning process into distinct task-specific phases, where the model is trained on one domain-specific task at a time rather than all tasks simultaneously. This sequential segmentation allows the model to specialize in each task without the interference of other tasks, thereby preventing catastrophic forgetting while maintaining versatility across multiple domains.
Solution Approach 2:
The patent applies preliminary action by performing interventionist fine-tuning before full fine-tuning. The interventionist phase uses a subset of training data and smaller update steps to prepare the model for subsequent tasks, establishing a foundation that protects previously learned knowledge while accommodating new domain-specific information.
2Reliability
If multiple separate models are trained for different domain-specific tasks, then each model achieves high accuracy on its specific task, but the overall system complexity and computing resource requirements increase
Solution Approach 1:
The patent merges multiple task-specific models into a single large language model through sequential fine-tuning. By combining the capabilities of what would otherwise require separate models into one unified model, the system reduces device complexity and computing resource requirements while maintaining high accuracy across multiple domain-specific tasks through the interventionist fine-tuning approach.
Solution Approach 2:
The patent creates a universal model that can perform multiple domain-specific tasks through interventionist fine-tuning. This single model is designed to be multi-functional, adapting to different tasks sequentially while retaining knowledge of previous tasks, thereby eliminating the need for multiple specialized models and reducing overall system complexity.
3Productivity
If conventional fine-tuning is applied to adapt the model to new domain-specific tasks, then the model learns new tasks efficiently, but performance on previously learned tasks is lost
Solution Approach 1:
The patent uses preliminary interventionist fine-tuning before full fine-tuning to prepare the model for new tasks while protecting previously learned knowledge. This preliminary phase uses controlled update steps and a subset of training data to introduce new domain-specific information without completely overwriting the model's existing knowledge base, thereby maintaining learning efficiency while preventing information loss.
Solution Approach 2:
The patent implements feedback mechanisms during fine-tuning by monitoring model performance on both new and previously learned tasks. This feedback allows for adjustment of learning rates and training parameters to ensure that new knowledge is acquired efficiently while previously learned tasks are preserved, creating a balanced fine-tuning process that prevents catastrophic forgetting.
Data Source
AI summary
Embodiments of the disclosed technologies are capable of training a large language model (LLM) to perform a first task type associated with a first task type using a first prompt comprising a task reasoning and an instruction associated with the task. The task reasoning comprises a set of guidelines associated with the task. The embodiments describe executing the LLM to perform the first task type. Performing the first task type comprises the LLM generating an output using the set of guidelines associated with the task. The embodiments describe executing the LLM to perform a second task type associated with the task using a second prompt. The second prompt comprises the instruction associated with the task. Performing the second task type comprises the LLM generating the output using the set of guidelines associated with the task