Adaptive Generative AI Feedback for Q&A Answer Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI-based Q&A systems lack effective quality control and adaptability, often providing irrelevant or inaccurately expressed answers due to reliance on a single reference source and lack of feedback integration.
Innovation Solution
An AI-based Q&A framework that incorporates feedback from human evaluators to adaptively learn and select the best candidate answers, utilizing multiple machine experts and adjusting reference sources based on cumulative feedback to enhance answer quality and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI is used to provide answers automatically, then productivity and cost-effectiveness are improved, but answer quality and relevance deteriorate due to lack of feedback integration
Solution Approach 1:
The system implements a feedback mechanism where user evaluations of answer quality are collected and used to retrain the generative AI model. This allows the system to maintain high productivity through automation while continuously improving answer quality based on actual user responses and corrections.
2Device complexity
If a single reference source is used for answer generation, then device complexity is reduced, but answer relevance and accuracy worsen
Solution Approach 1:
The system segments the reference source into multiple specialized knowledge bases organized by subject matter. Each knowledge base contains references specific to a particular domain, allowing the system to maintain manageable complexity while improving accuracy by selecting from multiple relevant sources rather than relying on a single reference.
3Adaptability or versatility
If machine experts are trained on diverse subjects, then adaptability is improved, but manufacturing precision and specialization worsen
Solution Approach 1:
The system divides the machine expert into multiple specialized sub-experts, each trained on specific subject matter. When a user asks a question, the system routes it to the appropriate specialized expert, maintaining both broad adaptability across subjects and precise knowledge within each domain.
Solution Approach 2:
Each machine expert is assigned specific local expertise in particular subject areas rather than general knowledge across all topics. This allows the system to maintain high adaptability by having multiple specialists while ensuring manufacturing precision through focused training in specific domains.
Data Source
AI summary
The present teaching relates to adaptive generative AI via feedback. Human evaluators evaluate an answer automatically generated by a machine expert in response to a question based on a reference from a source. The evaluation is relied on to update a fidelity metric for each human evaluator. A cumulative ranking of the answer is determined using the evaluation and the updated fidelity metric of each human evaluator. A fidelity attribute for the machine expert is updated based on the cumulative ranking. Feedback is created based on the answer, the question, the cumulative ranking, and the updated fidelity attribute for adapting the performance of the Q&A system.