Longform Technical QA Dataset Generation from Textbook Contexts
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Solution Overview
Problem
Existing benchmark datasets for Question and Answer Generation (QAG) primarily focus on rule-based or factoid question generation, failing to address dynamic longform technical questions and answers necessary for assessing technical knowledge in interviews.
Innovation Solution
A method and system for generating longform technical question and answer datasets using a rule-based preprocessing technique, optimized autoregressive language models, unsupervised template-based matching, and token matching scoring to create semantically complete and contextually relevant questions and answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based question generation methods are used, then question generation can be automated, but the questions are limited to factoid types and cannot generate dynamic longform technical questions
Solution Approach 1:
The patent combines rule-based preprocessing techniques with autoregressive language models to merge the advantages of both approaches. The rule-based component extracts structured contexts from technical documents, while the autoregressive model generates diverse longform questions, achieving both automation and question type versatility.
Solution Approach 2:
The autoregressive language model serves multiple functions: it generates questions of varying types (not just factoid), maintains consistency with source material through controlled generation, and adapts to different technical domains, making the system universally applicable for diverse question generation needs.
2Reliability
If human experts conduct technical interviews manually, then high-quality technical questions can be assessed, but the process is time-consuming and difficult to scale
Solution Approach 1:
The system creates synthetic question-answer pairs that replicate the quality and structure of expert-generated questions. By generating multiple variations and using autoregressive models to ensure coherence, the system produces copies of high-quality questions at scale without requiring proportional human expert input.
Solution Approach 2:
The system uses automated evaluation metrics and preprocessing techniques to assess and refine generated questions without continuous human intervention. The rule-based components and automated scoring mechanisms enable the system to self-evaluate question quality, maintaining reliability while enabling scaling.
3Quantity of substance
If existing benchmark datasets are used for training, then training data is readily available, but the datasets fail to address dynamic longform technical questions and answers
Solution Approach 1:
The system performs preliminary extraction of contexts and structures from technical documents before question generation. This preprocessing creates a foundation of structured technical information that enables the subsequent generation of longform questions with proper context, ensuring both availability and adaptability of training data.
Solution Approach 2:
The patent generates dynamic longform questions that adapt to different technical contexts and domains. The autoregressive model creates questions with varying complexity and structure based on the source material, transforming static benchmark datasets into dynamic, context-aware training data that covers longform technical questions.
Data Source
AI summary
Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score. Finally, a longform technical question and answer dataset is generated based on the combined plurality of questions and optimal answers.