Code Dataset Interpretation for Automated LLM Fine-Tuning
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Solution Overview
Problem
Existing methods face challenges in preparing code for fine-tuning Large Language Models (LLMs) unsupervisedly, ensuring generated code follows design patterns and coding styles, and managing dependencies without manual intervention.
Innovation Solution
A method and system for generating training data by analyzing code parameters, creating natural language interpretations, collating them, generating a conceptual explanation, and assigning labels to sub-datasets, automating the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual preparation of code for fine-tuning is performed, then code quality and design pattern adherence are improved, but time consumption and labor effort increase
Solution Approach 1:
The system enables self-service by automatically analyzing code datasets, generating natural language interpretations, creating conceptual explanations, and assigning labels without requiring manual human intervention. The automated fine-tuning preparation system performs tasks that would otherwise require expert human analysts, allowing the process to serve itself.
Solution Approach 2:
The patent replaces the mechanical manual process of code analysis and labeling with an automated computational system using machine learning models. The system uses NLP models to generate interpretations and explanations, substituting human cognitive mechanics with algorithmic processing.
2Reliability
If comprehensive code analysis is performed to ensure design pattern adherence, then code reliability is improved, but processing complexity increases
Solution Approach 1:
The code analysis process is segmented into distinct automated stages: generating natural language interpretations of code parameters, collating interpretations, creating conceptual explanations, and assigning labels to sub-datasets. This segmentation breaks down the complex task of ensuring design pattern adherence into manageable automated steps.
Solution Approach 2:
The system introduces natural language interpretations and conceptual explanations as intermediary representations between the raw code and the fine-tuning process. These intermediaries simplify the complexity by translating code structures into human-readable formats that capture design patterns and relationships.
3Measurement precision
If domain-specific custom data is used for fine-tuning, then model performance on specific tasks is improved, but data preparation difficulty increases
Solution Approach 1:
The system automatically prepares domain-specific training data by analyzing code datasets, generating interpretations, and creating labeled training examples without requiring manual data preparation. This self-service capability makes it easy to prepare custom domain data for fine-tuning.
Solution Approach 2:
The system performs preliminary actions by pre-processing code datasets into structured formats with natural language interpretations and conceptual explanations before fine-tuning. This preliminary preparation simplifies the subsequent fine-tuning process and ensures data is ready for domain-specific training.
Data Source
AI summary
The disclosure relates to a method and system of generating training data for fine-tuning of a Machine Learning (ML) model. The method includes generating one or more natural language interpretations of a dataset corresponding to one or more parameters associated with configuration of the dataset, and collating the one or more natural language interpretations of the dataset corresponding to one or more parameters, to generate a combined natural language interpretation of the dataset. The method further includes generating a conceptual explanation of the dataset, based on the combined natural language interpretation of the dataset, and assigning one or more labels to each sub-dataset of the dataset, based on the conceptual explanation of the dataset, to generate training data for fine-tuning of the ML model, wherein the dataset comprises a plurality of sub-datasets.