Automated Prediction of Examinee Proficiency from Short-Answer Questions (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to predicting examinee proficiency from short-answer questions (SAQs) use of labeled data to train on is difficult, and requires expensive expert-rated data. |
| Approach: | They propose a method to predict examinee proficiency from short-answer questions . previous approaches train on manually labeled data to predict human-ratings assigned to SAQs . |
| Outcome: | The proposed model examines examinee proficiency directly and does not require manual training on labeled data. |
Similar Papers
SkillQG: Learning to Generate Question for Reading Comprehension Assessment (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing question generation systems focus on the literal nature of questions and rarely consider comprehension types of the generated questions. |
| Approach: | They propose a question generation framework with controllable comprehension types for machine reading comprehension models. |
| Outcome: | Empirical results show that SkillQG outperforms baselines in quality, relevance, and skill-controllability while showing a performance boost in downstream question answering task. |
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction (2022.lrec-1)
Copied to clipboard
| Challenge: | Prior work has shown that QE models of grammatical error correction are biased toward data by learners with relatively high proficiency levels. |
| Approach: | They investigated whether learners' proficiency affects supervised quality estimation models of grammatical error correction (GEC) . they created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. |
| Outcome: | The proposed model is based on multiple proficiency levels and can be performed in real-world scenarios. |
Evaluating the Knowledge Dependency of Questions (2022.emnlp-main)
Copied to clipboard
Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, Seungtaek Choi
| Challenge: | Existing evaluation metrics for MCQ generation focus on the n-gram based similarity of the generated MCq to the gold sample and disregard their educational value. |
| Approach: | They propose to use a human survey to measure the MCQ’s answerability given knowledge of the target fact. |
| Outcome: | The proposed methods measure the MCQ’s answerability given knowledge of the target fact. |
Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions (2025.coling-main)
Copied to clipboard
Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos
| Challenge: | Multiple choice questions (MCQs) are often used in employee selection and training, but their creation is resource-intensive and requires significant effort and investment. |
| Approach: | They propose to use large language models and prompt engineering techniques to automate the generation and validation of MCQs. |
| Outcome: | The proposed system reduces the burden on human resources and enables scalable, cost-effective MCQ generation. |
Knowing More About Questions Can Help: Improving Calibration in Question Answering (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on calibration focuses on model confidence, such as the max probability of the predicted class. |
| Approach: | They propose a calibration method which estimates whether model correctly predicts answer for each question. |
| Outcome: | The proposed calibration method achieves 5-10% gains on reading comprehension benchmarks. |
AskQE: Question Answering as Automatic Evaluation for Machine Translation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing MT error detection and quality estimation (QE) techniques do not address this practical scenario. |
| Approach: | They propose a question generation and answering framework that detects critical MT errors and provides actionable feedback to help users decide whether to accept or reject MT outputs even without the knowledge of the target language. |
| Outcome: | The proposed framework has higher Kendall’s Tau correlation and decision accuracy with human ratings compared to other QE metrics. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
Copied to clipboard
| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)
Copied to clipboard
| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
| Approach: | They propose to use large language models to analyze and score written essays using four different prompts. |
| Outcome: | The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models. |
Can NLI Models Verify QA Systems’ Predictions? (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent question answering systems perform well on benchmark datasets, but are not always well-calibrated to spot spurious answers under distribution shifts. |
| Approach: | They propose to use natural language inference to verify whether answers are correct . they leverage large pre-trained models and recent prior datasets to construct powerful question conversion and decontextualization modules. |
| Outcome: | The proposed approach improves the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. |
Precise Model Benchmarking with Only a Few Observations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Accurate evaluation of large language models is crucial for identifying their strengths and weaknesses. |
| Approach: | They propose an empirical Bayes estimator that balances direct and regression estimates for each subgroup separately, improving the precision of subgroup-level estimates of model performance. |
| Outcome: | The proposed model reduces the mean squared error by up to 50% on multiple datasets. |