Interview Question Generation Using Speculative NLP Processing
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Solution Overview
Problem
Existing interview systems lack flexibility in generating questions, as they rely on predefined question lists, limiting the ability to adapt to user responses.
Innovation Solution
A computer system that includes speculative execution processing to generate additional questions using a natural language processing program, allowing for flexible question generation based on user answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predefined question list is used for interview questioning, then the system structure is simple and easy to manage, but the flexibility and adaptability of question generation is limited
Solution Approach 1:
The system performs speculative execution processing to generate candidate questions in advance during the reception of stream data, before the actual interview flow requires them. This preliminary generation of question candidates based on received answers allows the system to quickly select and present appropriate additional questions without complex real-time decision-making, thus improving flexibility while managing complexity
Solution Approach 2:
A natural language processing program is introduced as an intermediary between the received stream data and the question generation process. This NLP program processes the received answers and generates candidate questions, acting as a mediator that translates user responses into appropriate follow-up questions, thereby enhancing adaptability without requiring complex manual rule systems
2Adaptability or versatility
If speculative execution processing is performed during reception of stream data, then the flexibility and quality of additional questions improve, but the processing time and computational load increase
Solution Approach 1:
The system performs speculative execution processing during the reception of stream data, generating candidate questions in advance before they are actually needed in the interview flow. By performing this processing preliminarily and in parallel with data reception, the system avoids time-consuming operations during critical interview moments, thus improving adaptability while minimizing time loss
Solution Approach 2:
The system generates multiple candidate questions through speculative execution, creating more questions than may ultimately be needed. This excessive generation of candidates allows for quick selection of the most appropriate question without time pressure, improving adaptability while the actual time cost is distributed across the speculative processing period rather than concentrated during interview delivery
Data Source
AI summary
In order to flexibly present an additional question in response to an answer from a user, a computer system outputs output data for presenting a first question to a user, sequentially receives first stream data including an answer to the first question from the user, executes speculative execution processing for question candidate generation at least one time during reception of the first stream data, generates a second question based on a question candidate generated by the speculative execution processing for first question candidate generation when input of the answer to the first question is ended, and outputs output data for presenting the second question to the user. In the speculative execution processing for the question candidate generation, a prompt that causes a natural language processing program to execute generation of the question candidate in consideration of the answer included in the stream data received is generated, the prompt is input to the natural language processing program, and the question candidate generated by the natural language processing program is stored in a storage medium.