DSL Planning for Accurate Long-Task Content Generation
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Solution Overview
Problem
Large language models (LLMs) face challenges with hallucinations, accuracy maintenance, limited long-term memory, and inability to execute complex, multi-step tasks coherently, impacting their reliability and utility in sophisticated applications requiring sustained attention and task continuity.
Innovation Solution
A computing system utilizing processing circuitry and memory to generate domain-specific language (DSL) plans, convert them into executable code, and execute this code in a specialized environment to produce coherent and accurate content, leveraging DSLs like SQL, HLSL/GLSL, Terraform, MATLAB, R, machine learning languages, and Cucumber, and interacting with generative models and agents to ensure task continuity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional LLMs generate longer texts or handle more complex tasks, then their functionality and task coverage improve, but hallucinations and inaccuracies accumulate, significantly impacting output reliability
Solution Approach 1:
The patent introduces an intermediary verification mechanism that acts as a mediator between the LLM's generative capabilities and the final output. This intermediary layer checks and validates generated content against factual data sources, preventing hallucinations from propagating to the final output while preserving the LLM's ability to handle complex tasks.
Solution Approach 2:
The system implements a feedback loop where generated outputs are continuously verified against ground truth data. When inaccuracies or hallucinations are detected, the system provides feedback to correct or reject the generated content, thereby maintaining high reliability even as task complexity increases.
2Device complexity
If LLMs operate with a limited intent window to maintain model simplicity, then device complexity is reduced, but the ability to handle long-term projects requiring sustained attention and consistency is restricted
Solution Approach 1:
The patent segments long-term tasks into smaller, manageable sub-tasks that can be processed within the LLM's intent window. Each sub-task is handled independently with consistent instructions propagated through the segmentation boundaries, enabling the system to maintain task continuity without requiring the model to remember entire project histories.
Solution Approach 2:
The system performs preliminary action by establishing comprehensive task instructions and context before processing begins. These pre-established guidelines are then repeatedly applied across multiple processing steps, ensuring consistency throughout long-term projects without requiring the model to maintain long-term memory of all previous states.
3Ease of operation
If LLMs are designed to operate independently without external verification, then ease of operation is improved, but the tendency to generate hallucinations and inaccurate information increases
Solution Approach 1:
The system implements self-service by enabling the LLM to automatically verify its own generated outputs against factual data sources. The model independently checks its work without requiring external human verification, maintaining ease of operation while improving factual accuracy through automated self-correction mechanisms.
Data Source
AI summary
A computing system is provided, comprising processing circuitry and associated memory. The processing circuitry is configured to receive a prompt including a message as natural language input from an interaction interface, extract an intent of the message, and select a domain-specific language (DSL) domain corresponding to the intent of the message. The processing circuitry then generates a DSL plan encoded in a DSL based on the message and the selected DSL domain, generates code based on the message and the generated DSL plan, executes the code in a code execution environment to generate content corresponding to the message and the selected DSL domain, and outputs the generated content.