SoftSkillQG: Robust Skill-Targeted Reading ComprehensionQuestion Generation Using Soft-prompts
Bibliographic record
Abstract
It takes skilled teachers a significant amount of time and effort to create high quality reading comprehension questions, often making it impractical to target a particular reader’s weaknesses. Recently, language models have been proposed as a tool to help teachers fill this gap, allowing teachers to generate questions to target specific skill types. However, state of the art methods rely on manually crafted answers or prompts. Yet, these methods still do not achieve the same quality as manually written questions. In this paper, we propose SoftSkillQG, a new soft-prompt based language model for generating skill-targeted reading comprehension questions that does not require any manual effort to target new skills. We compare SoftSkillQG against a variety of strong baselines and show that SoftSkillQG outperforms existing techniques on the SBRCS dataset and has better Context Specificity without requiring any manual effort to target new skills. Finally, we analyse how well various automatic question evaluation methods capture diversity and align with human judgments. From this analysis, we find that multiple evaluation methods may be required to capture different aspects of question quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".