Language Model Function Determination for Low-Latency Software Execution
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Solution Overview
Problem
Large language models (LLMs) in software development face issues with accuracy, reliability, programming latency, resource usage, and maintainability, leading to inefficiencies and environmental concerns.
Innovation Solution
A language model is trained to determine software functions from natural language queries, supplemented with reduced context information, acting as a super-agent to manage workflows autonomously and reduce latency, with fine-tuning for on-device deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large language models are used to generate code and suggest solutions, then coding acceleration is improved, but programming latency increases due to complex queries and extensive code generation
Solution Approach 1:
The patent segments the code generation process into multiple stages: (1) extracting relevant context information from codebases, (2) generating simplified function signatures, (3) executing code locally, and (4) providing explanations. This segmentation allows the system to handle complex queries systematically without overwhelming latency, as each stage processes information in manageable portions rather than attempting to generate entire codebases at once.
Solution Approach 2:
The system performs preliminary actions by pre-extracting and indexing relevant context information from codebases before actual code generation is needed. Function signatures and code explanations are prepared in advance and stored for quick retrieval during query processing, eliminating the need to search through entire codebases in real-time and significantly reducing programming latency.
2Reliability
If context information is provided jointly with queries to the language model, then accuracy is improved, but resource usage increases and programming latency increases
Solution Approach 1:
The patent extracts only the necessary context information relevant to each specific query from the entire codebase. Instead of providing all available context to the language model, the system uses retrieval mechanisms to select and extract only the function signatures and code excerpts that are actually needed to answer the user's question, significantly reducing computational resource usage while maintaining accuracy.
Solution Approach 2:
The system applies local quality by providing context information selectively based on the specific query and codebase structure. Different portions of the codebase are weighted differently based on their relevance to the current query, allowing the language model to focus computational resources on the most important context rather than processing all available information uniformly.
3Reliability
If context information is provided jointly with queries to the language model, then accuracy is improved, but programming latency increases
Solution Approach 1:
The system performs preliminary extraction and indexing of context information from codebases before queries are received. Function signatures, code explanations, and relevant metadata are pre-processed and stored in optimized data structures that enable rapid retrieval during query processing, eliminating the need to scan through entire codebases in real-time and significantly reducing programming latency while maintaining high accuracy.
Solution Approach 2:
The patent introduces intermediary components including a retrieval module and context indexer that mediate between the user query and the language model. These intermediaries pre-process and filter context information, presenting only the most relevant snippets to the language model in an optimized format, thereby reducing both the time and computational resources required for accurate code generation.
4Adaptability or versatility
If LLMs are trained and deployed with substantial computational power, then functionality is improved, but costs increase and scalability is limited
Solution Approach 1:
The patent uses smaller language models that have been fine-tuned on domain-specific data rather than deploying large-scale pre-trained models. These smaller models can be copied and deployed across multiple devices and codebases efficiently, reducing the computational infrastructure complexity while maintaining high functionality through targeted training on relevant programming contexts and functions.
Solution Approach 2:
The system adapts model parameters dynamically based on the specific query and codebase context rather than using fixed large-scale models. The language model adjusts its processing based on the complexity of the query and the amount of context information retrieved, allowing efficient resource utilization while maintaining high adaptability to different programming tasks and codebases.
Data Source
AI summary
This application is directed to implementing functions at a computer system automatically. The computer system receives a natural language query. In response to the natural language query, the computer system automatically applies a function determination model to generate function information of a target function based on the natural language query. The function information further includes identification information and one or more parameters of the target function. The target function is implemented based on the function information. One or more user applications are configured to implement a plurality of predefined functions including the target function.