Question Answering Pipeline for Hallucination Reduction
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Solution Overview
Problem
Existing question answering (QA) systems face challenges with hallucinations, outdated knowledge, and high operational costs, particularly with large language models that struggle to handle diverse types of complex questions effectively.
Innovation Solution
A QA system and method based on a complex reasoning pipeline that classifies input questions by reasoning type, decomposes questions into sub-questions, and utilizes optimized reasoning pipelines, including search and document reading modules, to provide accurate answers while reducing operational costs by using small or medium-sized language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large language models are used to improve QA performance, then answer accuracy and knowledge coverage are improved, but operational costs and computational resources increase significantly
Solution Approach 1:
The patent segments the QA task into multiple reasoning steps (understanding, planning, searching, answering) and processes them sequentially through a pipeline architecture. This allows using smaller, more efficient models for each segment rather than relying on a single large language model for the entire task, thereby reducing operational costs while maintaining answer accuracy.
2Adaptability or versatility
If large language models are used to handle diverse QA types, then versatility and reasoning capability are improved, but model size and computational requirements increase
Solution Approach 1:
The patent creates a universal reasoning pipeline that can handle multiple types of QA tasks (single-hop, multi-hop, bridge-type questions) through a standardized multi-stage process. This pipeline integrates various specialized components (understanding module, planning module, search module, answer generation module) that work together to provide versatile reasoning capabilities without requiring separate large models for each task type.
3Measurement precision
If complex reasoning pipelines are implemented to improve multi-hop QA performance, then reasoning accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The patent implements a dynamic reasoning pipeline that adapts its complexity based on the specific QA task. The system can adjust the depth of reasoning steps, select appropriate search strategies, and modify processing intensity according to the question type and complexity, thereby achieving high reasoning accuracy without consistently incurring maximum system complexity.
Solution Approach 2:
The complex reasoning process is divided into distinct modular stages (question understanding, reasoning plan generation, information searching, answer synthesis). Each stage can be independently optimized and executed, making the overall complex pipeline more manageable and efficient while maintaining high reasoning accuracy through specialized processing at each step.
4Use of energy by stationary object
If fine-tuned small and medium-sized language models are used, then operational costs are reduced, but handling capability for various QA types may be limited
Solution Approach 1:
The patent combines multiple fine-tuned small and medium-sized language models into a collaborative pipeline system. Each model is specialized for specific subtasks (e.g., one for question understanding, another for search query generation, another for answer synthesis), and their outputs are integrated to achieve comprehensive QA handling capability that rivals or exceeds single large models while maintaining lower operational costs.
Data Source
AI summary
Provided are a question answering (QA) system and method based on a complex reasoning pipeline. The QA method includes classifying a reasoning type of an input question, generating sub-questions by decomposing the input question, inputting the sub-questions to a reasoning pipeline corresponding to the reasoning type, and outputting a final answer corresponding to the input sub-questions from the reasoning pipeline.