Long-Form Technical Question Difficulty Prediction With Weak Supervision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting the difficulty of technical interview questions require large volumes of candidate response data and are not effective for creating question banks in new domains, as they rely on traditional deep learning models and are limited to multiple-choice or factoid questions, failing to address the open-ended and long-form nature of technical interviews.
Innovation Solution
A method and system using weak supervision to generate long-form technical questions from domain-related textbooks, employing unsupervised template-based techniques and zero-shot pretrained large-language models, combined with Bloom's Taxonomy to compute context and task difficulty, and train supervised question difficulty prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning models are used to predict question difficulty, then prediction accuracy can be improved, but large volumes of candidate response data are required which makes the process impractical for new domains
Solution Approach 1:
The system performs preliminary actions by generating synthetic candidate responses and difficulty labels using LLMs and template-based methods before actual field tests. This creates a pre-training dataset that allows the model to learn difficulty prediction patterns without requiring extensive real candidate response data, thus resolving the contradiction between needing high accuracy and having limited data for new domains
Solution Approach 2:
The system creates copies of actual questions with synthetically generated responses and difficulty labels using LLMs. These synthetic copies serve as training data that mimics real candidate responses, enabling the model to be trained effectively without requiring large volumes of actual candidate response data from field tests
2Measurement precision
If field tests with real students are conducted to create question banks, then accurate difficulty labels can be obtained, but time and resources are consumed
Solution Approach 1:
The system performs preliminary difficulty labeling using LLMs and template-based methods before conducting field tests. This pre-labeling provides initial difficulty estimates that can be used immediately or refined later, eliminating the need to wait for field test results to obtain difficulty labels and significantly reducing the time loss
Solution Approach 2:
The system uses LLMs to generate synthetic difficulty labels that copy the format and structure of actual difficulty annotations. These synthetic labels provide immediate difficulty information without requiring time-consuming field tests with real students, thus resolving the contradiction between accurate labeling and time consumption
3Adaptability or versatility
If traditional approaches are used for question difficulty prediction, then multiple-choice and factoid questions can be handled, but long-form open-ended technical interview questions cannot be effectively addressed
Solution Approach 1:
The system changes the parameters of the prediction approach by using LLMs that are specifically tuned for long-form question understanding and by modifying the difficulty labeling process to account for open-ended responses. This allows the system to reliably handle long-form technical interview questions while maintaining prediction reliability, resolving the contradiction between question type versatility and prediction reliability
Data Source
AI summary
Technical interviewing is important for organizations for assessing a candidate to make hiring decision. For effective technical interviewing, predicting difficulty of long form technical questions is crucial. The present disclosure provides systems and methods for predicting difficulty of long form technical questions using weak supervision from textbooks. Further, zero shot pre-trained large language models and unsupervised template-based technique are used for generating questions. Furthermore, a difficulty score is assigned to the generated questions based on context difficulty and task difficulty. The context difficulty for the generated questions is computed using hierarchical structure of the textbooks, and the task difficulty is computed by determining a similarity between the generated questions and Bloom's taxonomy levels. In the present disclosure, few supervised question difficulty prediction models are trained by means of weak supervision using the generated questions and corresponding difficulty scores and further evaluated for prediction performance using a gold-standard question difficulty dataset.