Papers with educational testing
Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)
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| Challenge: | Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context. |
| Approach: | They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction. |
| Outcome: | The proposed method generates better questions according to standard metrics when compared to several baselines. |
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above (2025.acl-long)
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| Challenge: | Multiple choice question answering (MCQA) is popular for LLM evaluation due to its simplicity and human-like testing. |
| Approach: | They argue for a reform of multiple choice question answering (MCQA) they argue for more generative formats based on human testing . |
| Outcome: | The proposed reforms improve the quality of MCQA, the authors argue . they show that even when MCQ is a useful format, its datasets suffer from leakage, unanswerability, shortcuts and saturation. |
Revisiting Generalization Across Difficulty Levels: It’s Not So Easy (2026.eacl-long)
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| Challenge: | Existing research is mixed regarding whether training on easier or harder data leads to better results. |
| Approach: | They examine how well large language models generalize across different task difficulties by using a large dataset and a well-established difficulty metric. |
| Outcome: | The results show that training on hard data can't achieve consistent improvements across the full range of difficulties. |
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021.acl-long)
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| Challenge: | Rather than replacing leaderboards, we advocate a re-imagining of the model to highlight if and where progress is made. |
| Approach: | They propose a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. |
| Outcome: | The proposed model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. |
None of the Above, Less of the Right Parallel Patterns in Human and LLM Performance on Multi-Choice Questions Answering (2025.findings-acl)
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| Challenge: | Multiple-choice exam questions with “None of the above” (NA) options have been extensively studied in educational testing . however, their impact on Large Language Models (LLMs) evaluation remains underexplored . |
| Approach: | They conduct systematic experiments with 28 LLMs on the MMLU benchmark to examine how NA options affect model performance and confidence calibration. |
| Outcome: | The results highlight important implications for benchmark design and raise questions about LLMs’ ability to handle uncertainty in real-world applications. |