Modular Question Resolution Pipeline for Complex Query Reliability
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Solution Overview
Problem
Traditional monolithic machine learning models struggle with complex, hierarchical queries due to their inability to handle diverse sub-questions and long input documents, leading to unreliable outputs and limited input flexibility.
Innovation Solution
A modular machine learning pipeline comprising a retrieval ensemble model and a machine learning aggregation model, trained partially independently and end-to-end, to break down complex queries into manageable sub-problems, with continuous evaluation for accuracy improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic machine learning model is used for query processing, then the model structure is simple, but the model cannot handle complex hierarchical queries and diverse sub-questions effectively
Solution Approach 1:
The patent divides the complex query processing task into multiple independent modules: a retrieval module that handles information extraction, a reasoning module that processes logical relationships, and an aggregation module that synthesizes final answers. Each module can be independently trained and optimized for specific sub-tasks, enabling the system to handle diverse question types while maintaining manageable complexity through modular architecture.
2Reliability
If a monolithic machine learning model is used, then the system is easier to implement, but the reliability of output is limited
Solution Approach 1:
The patent implements a segmented processing pipeline where each module (retrieval, reasoning, aggregation) performs a specific function with high reliability. The retrieval module focuses on accurate information extraction, the reasoning module on logical inference, and the aggregation module on synthesis. This division allows each component to be optimized for its specific task, improving overall output reliability while the modular structure keeps implementation complexity manageable through clear separation of concerns.
Solution Approach 2:
The patent incorporates feedback mechanisms where the output of each module is evaluated and used to refine subsequent processing. The reasoning module receives feedback from the retrieval module about information quality, and the aggregation module receives feedback from the reasoning module about inference confidence. This feedback loop continuously improves output reliability by identifying and correcting errors at each stage of the pipeline.
3Adaptability or versatility
If a monolithic model is tailored for specific tasks, then the model is specialized, but it struggles with complex hierarchical arrangements of tasks
Solution Approach 1:
The patent designs universal modules that can handle multiple query types through standardized interfaces. The retrieval module can extract information from various document formats, the reasoning module can process different logical relationships (temporal, causal, hierarchical), and the aggregation module can synthesize answers in multiple formats. This multi-functionality enables the system to generalize across diverse query types while maintaining processing efficiency through consistent module interactions.
Data Source
AI summary
Various embodiments of the present disclosure provide machine-learning question resolution techniques for improving question response outputs. The techniques may include receiving a plurality of evidence passages from a document set corresponding to an input question. The techniques may include generating, using a retrieval ensemble model, a plurality of evidence predictions for an evidence passage of the plurality of evidence passages based on the input question. The techniques may include generating, using the retrieval ensemble model, a weighted aggregate prediction for the evidence passage based on the plurality of evidence predictions. The techniques may include selecting, a set of input passages from the plurality of evidence passages based on the weighted aggregate prediction. The techniques may include generating, using a machine learning aggregation model, a question response based on the set of input passages and the input question. The techniques may include providing the question response.