Question Set Generation for Full Document Coverage
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Solution Overview
Problem
Existing technologies struggle to generate a set of questions that comprehensively cover the entire document, as they lack a mechanism to adjust the number of questions generated for each chunk of content, leading to incomplete coverage.
Innovation Solution
An information processing apparatus and method that determines the similarity of content for multiple question sentences generated by a generative model and generates a set of question sentences based on this determination, ensuring comprehensive coverage of the document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a generative model is used to automatically generate question sentences from a document, then text generation efficiency is improved, but the coverage of questions across the entire document deteriorates due to lack of adjustment mechanism for different chunks
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates the coverage of generated questions against the original document structure. The determination unit assesses whether generated questions adequately represent different chunks of the document, and this evaluation feedback is used to adjust the generation process, ensuring comprehensive coverage while maintaining efficient automatic generation.
Solution Approach 2:
The system dynamically adjusts the number of questions generated for each document chunk based on the chunk's content characteristics and importance. Rather than using a fixed generation strategy, the system adapts the question generation process to match the varying information density and significance across different portions of the document, thereby improving overall coverage.
2Loss of information
If the number of questions generated for each chunk is increased to improve coverage, then document coverage is improved, but the complexity of the generation system worsens due to lack of adjustment mechanism
Solution Approach 1:
The system performs self-evaluation through the determination unit, which automatically assesses the coverage quality of generated questions without requiring external manual intervention. This self-service mechanism allows the system to autonomously identify coverage gaps and adjust its generation strategy, achieving comprehensive coverage without adding significant external complexity.
Solution Approach 2:
The patent incorporates a preliminary evaluation step where the system analyzes the document structure and identifies key chunks before question generation. This preliminary action allows the system to pre-determine the optimal number of questions for each chunk, simplifying the overall generation process by avoiding the need for complex real-time adjustments during question creation.
3Ease of operation
If uniform question generation is applied to all document chunks, then the ease of operation is improved, but the manufacturing precision of question distribution deteriorates
Solution Approach 1:
The patent applies local quality by treating different document chunks differently based on their specific characteristics. Instead of uniform question generation, the system determines the appropriate number of questions for each chunk based on its content importance, information density, and relevance to the overall document theme. This localized approach ensures precise question distribution while maintaining operational simplicity through automated determination.
Data Source
AI summary
A set of questions covering the entire target document is generated. An information processing apparatus includes: a determination unit that determines similarity of content for multiple question sentences regarding the content of a target document, which are generated by a generative model trained to generate question sentences regarding the content of a document; and a set generation unit that generates a set of question sentences including the multiple question sentences generated by the generative model based on a determination result of the determination unit. Thus, for example, it is also possible to generate a set of question sentences optimized for a Q&A collection or for training data of the generative model.