Chain-of-Thought Response Generation for Complex BI Questions
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Solution Overview
Problem
Existing natural language processing systems struggle to understand and respond to complex business intelligence questions due to non-uniform semantic representations and lack of domain-specific reasoning, particularly in conversational contexts.
Innovation Solution
A system employing a chain-of-thought reasoning approach using a large language model (LLM) and a set of algorithms to break down complex questions into manageable tasks, execute these tasks sequentially, and provide explanations for each step, leveraging a vector database for improved accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP systems use standard processing approaches, then system simplicity is maintained, but response accuracy to complex business intelligence questions deteriorates
Solution Approach 1:
The patent divides complex business intelligence questions into multiple sub-tasks (e.g., data extraction, analysis, visualization, reporting) that can be processed sequentially by specialized algorithms. This segmentation allows each algorithm to focus on a specific aspect of the question, improving overall response accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces a chain-of-thought reasoning mechanism as an intermediary between the input question and the final response. This intermediary component breaks down the reasoning process into explicit steps, allowing the system to handle complex queries by processing information incrementally through intermediate representations rather than attempting to solve the entire problem at once.
2Measurement precision
If the system processes complex questions with detailed reasoning, then response relevance improves, but processing time increases
Solution Approach 1:
By segmenting the question processing into distinct sub-tasks (data extraction, analysis, visualization, reporting), the system can process each segment in parallel or sequentially with optimized algorithms, reducing overall processing time while maintaining detailed reasoning for each segment to ensure high response relevance.
Solution Approach 2:
The system performs preliminary actions by pre-defining the chain-of-thought reasoning steps and pre-processing common business intelligence operations. This allows the system to quickly retrieve and apply pre-computed analysis frameworks when processing new questions, reducing the time required for complex reasoning while maintaining relevance through contextual adaptation.
3Loss of information
If the system provides detailed explanations for task selection, then user understanding improves, but information output volume increases
Solution Approach 1:
The patent segments the explanation output into correspond with the segmented task execution. Each sub-task has a corresponding explanation component that provides concise rationale only for that specific step, avoiding redundant information while ensuring complete user understanding of the reasoning process through structured, modular explanations.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to response generation based on chain-of-thought reasoning. For example, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise a task determination component that can determine one or more tasks to be executed to generate a response to a question. The computer executable components can further comprise a task execution component that can execute a task of the one or more tasks based on an output of a previously executed task.