Pith. sign in

REVIEW 4 major objections 5 minor 13 references

Advancing Question Generation with Joint Narrative and Difficulty Control

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper shows that a single fine-tuned Flan-T5-large can simultaneously control the narrative element and difficulty tier of generated reading-comprehension QA pairs, with reliable difficulty separation at easy, hard, and extreme…

desk verdict Honest, modest feasibility study for joint narrative and difficulty control; narrative evidence is solid, difficulty evidence is weakened by a circular simulated-learner setup. read the letter →

arxiv 2506.06812 v1 pith:NHLVGPJ2 submitted 2025-06-07 cs.CL

Bernardo Leite , Henrique Lopes Cardoso This is my paper · ORCID
classification cs.CL
keywords questiongenerationdifficultycontrolnarrativeitemresponsetheoryreadingcomprehensioneducationalNLPFlan-T5FairyTaleQA
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether one modest-scale model—Flan-T5-large—can generate reading-comprehension question–answer pairs that obey two user-supplied attributes at once: a narrative element (such as causal relationship, character, or prediction) and a difficulty level (easy through extreme). To get difficulty labels without human annotators, the authors answer all FairyTaleQA questions with five SQuAD-trained QA systems of different abilities, then fit a Rasch Item Response Theory model to the resulting binary responses and bucket the estimated difficulties into five tiers. The answer is a qualified yes: narrative control is consistently achieved, difficulty control works well when conditioned alone, and the joint strategy separates easy from hard and extreme clearly but confuses the middle two tiers. The result matters for educational question generation, where tailoring both what a question asks and how hard it is would let a single system produce personalized practice items.

What carries the argument

The load-bearing machinery is the Rasch model of Item Response Theory, which converts the binary correctness matrix produced by five simulated-learner QA systems (DeBERTaV3, RoBERTa, BERT, DistilBERT, GPT-2; ability estimates from −1.60 to 0.43) into a per-question difficulty parameter $b$, via $P(X=1 \mid \theta, b)=e^{\theta-b}/(1+e^{\theta-b})$, normalized to five labels (easy, medium, moderate, hard, extreme). Those labels, together with FairyTaleQA's seven expert narrative annotations, are injected into a prompt template that conditions Flan-T5-large; the model is fine-tuned on the augmented dataset and decodes with top-$k$ sampling ($k=50$, $p=0.9$, temperature 1.2). The argument that control works is carried by two comparisons: narrative control is measured by ROUGE-L F1 and BLEURT similarity between generated and human-authored questions, and difficulty control by the percentage of correct answers from the simulated learners across requested difficulty tiers. A secondary mechanism is the PINC metric (paraphrase in n-gram changes), which quantifies lexical novelty and is shown to increase with difficulty.

What would settle it

Have a group of students with known reading ability answer the 1,970 generated QA pairs (or a stratified subset) and compute the proportion of correct answers per requested difficulty tier and narrative attribute. If the easy-to-extreme ordering does not appear in students' responses, or if students' error patterns differ substantially from those of the simulated-learner QA systems, then the difficulty labels and the reported difficulty control lack external validity for real learners.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that joint narrative and difficulty control is feasible in a single fine-tuned encoder–decoder model: given a prompt of the form 'Generate a ⟨d⟩ question-answer pair about narrative label ⟨n⟩ considering the following text: ⟨t⟩', Flan-T5-large produces questions whose narrative content resembles human-authored ground truth (ROUGE-L F1 and BLEURT both rise over a text-only baseline) and whose difficulty, measured by the percentage of simulated learners answering correctly, falls monotonically as the requested difficulty rises from easy to hard to extreme. The qualification the authors emphasize is that the middle tiers (medium and moderate) are not reliably separated under joint control, and that the effect varies by narrative attribute: causal and outcome questions show the most consistent difficulty ordering, while prediction and feeling questions—the rarest in FairyTaleQA—show the least. The paper also reports that reducing the difficulty granularity from five levels to three restores consistency, and that lexical novelty of generated pairs relative to the source text rises with difficulty, with 14% of a sampled hundred pairs containing hallucinated content and 10% being nonsensical.

Load-bearing premise

The load-bearing assumption is that five SQuAD-trained QA systems (DeBERTaV3, RoBERTa, BERT, DistilBERT, GPT-2) approximate how real students answer narrative questions, so that IRT difficulty estimates computed from their correctness and the difficulty-control evaluation both rest on this proxy; the authors acknowledge that real student data are still needed.

Editorial extensions

If this is right

  • A single fine-tuned model can offer both content control and difficulty control at once, so an educational QG system can generate personalized practice items without separate pipelines for narrative and difficulty.
  • Difficulty labels can be produced automatically for any QA dataset via the simulated-learner plus IRT pipeline, removing the need for human difficulty annotation at scale.
  • Joint control is reliable at the extremes of the difficulty scale; systems aiming for fine-grained intermediate difficulty should expect confusion, and coarser three-level difficulty labels restore consistency.
  • Hallucinated content (14% of sampled outputs) probably inflates measured difficulty, since QA systems fail on questions about invented entities; evaluating controllability must therefore be paired with a hallucination check.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the same QA systems that produce the IRT difficulty labels also measure whether difficulty control worked, the two evaluation stages are coupled; independent learner data would be a stronger check on the difficulty claims.
  • The PINC-vs-difficulty trend suggests a cheap, model-free proxy for difficulty control: lexical novelty relative to the source text could be monitored during decoding without invoking QA systems, though the paper does not itself claim this.
  • The flattening of the difficulty slope under joint control is likely a general phenomenon: when one control attribute constrains the content of the question, the space of possible questions shrinks, so the other attribute has less room to vary; similar joint-control systems should expect the same trade-off.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a joint narrative and difficulty control strategy for question-answer pair generation from narrative texts. It augments FairyTaleQA with IRT-based difficulty labels estimated from binary correctness of five simulated-learner QA systems (DeBERTaV3, RoBERTa, BERT, DistilBERT, GPT-2), then fine-tunes Flan-T5-large on four data setups (text-only, narrative+text, difficulty+text, narrative+difficulty+text). Narrative control is evaluated by ROUGE-L F1 and BLEURT similarity to human-authored ground-truth questions; difficulty control is evaluated by the correctness of the same five simulated learners on generated QA pairs across difficulty levels. The paper reports that narrative control is consistently effective, difficulty control alone shows a monotonic decrease in correctness, joint control is generally effective at easy, hard, and extreme levels but inconsistent at medium and moderate levels, and that reducing difficulty granularity improves consistency. The authors frame the results as preliminary evidence of feasibility.

Significance. The topic addresses a genuine gap in controllable question generation by combining two control dimensions. The paper has several strengths: it uses an established dataset with expert narrative annotations, adopts a principled IRT framework, includes an error analysis and a linguistic-feature analysis (PINC), and ablate difficulty granularity. If the difficulty-control evidence were externally validated, the contribution would be useful for educational question generation. However, the significance of the difficulty-control claims is currently tempered by the circularity between label construction and evaluation, the very small number of simulated learners used for IRT estimation, and the absence of statistical inference. The narrative-control results, while not subject to circularity, also lack significance testing.

major comments (4)
  1. [Section 4.4 and Section 5.2] The difficulty labels used for training and the evaluation instrument for difficulty control are the same five simulated-learner QA systems. Section 4.4 constructs the IRT difficulty labels from the binary response matrix of these systems; Section 5.2 then measures difficulty control by the correctness of the same systems on the generated QA pairs (Figures 3-6). Under this protocol, the monotonic ordering of correctness across difficulty levels is partly by construction: the systems' systematic biases (e.g., specific failure modes on 'why' questions) enter both the label definition and the outcome measure. This shared-bias problem is acknowledged in the Limitations section only with respect to the use of simulated learners generally, not with respect to the circularity. The difficulty-control claim would be substantially strengthened by evaluating on held-out QA systems not used in label creation, or on human student responses.
  2. [Section 4.4] The Rasch model is fit to a binary response matrix with only five simulated learners as respondents. With five respondents per question, the item difficulty parameter has very little information; the five normalized difficulty values (0, 0.28, 0.50, 0.72, 1) correspond roughly to sum scores of the five systems, and the labels are effectively bins on a count from 0 to 5. The paper reports no standard errors, item fit statistics, or test-retest/stability analysis of the estimated difficulties. Consequently, the observed confusion between medium and moderate levels in the joint model (Figures 4-5) may be an artifact of noisy labels rather than a genuine property of joint control. Please report the response matrix size, the distribution of sum scores, and label-stability checks (e.g., bootstrap resampling of questions or leave-one-learner-out estimation).
  3. [Section 5.2] The claims that narrative control improves similarity to ground truth (Table 4 and Table 6) and that difficulty control is 'consistent' at easy, hard, and extreme but 'inconsistent' at medium and moderate (Figures 3-6) are based on descriptive statistics without significance tests, confidence intervals, or effect sizes. For example, in Figure 4 the difference between medium and moderate for DeBERTaV3 is 44.6 vs 44.6, and for RoBERTa 38.7 vs 39.3, an inversion; these differences are within the range of sampling noise for the number of generated QA pairs. The paper should provide bootstrap or paired statistical tests across the generated QA pairs for the key comparisons, or at least report the number of items per cell and confidence intervals.
  4. [Section 4.6] The evaluation protocol in Section 4.6 states that five QA pairs are generated for each test section (one per difficulty level) and that the approach 'ensures that the generated QA pairs are balanced across distinct difficulty levels and narrative elements,' but it does not specify how the narrative label is chosen for each generated pair. Since the joint model is conditioned on a narrative label, the per-narrative difficulty-control analysis in Figure 5 depends on a defined mapping from sections to narrative labels. Without this, the number of generated QA pairs per (narrative, difficulty) cell is unclear, and the lower consistency reported for 'prediction' and 'feeling' may simply reflect fewer examples. Please clarify the inference protocol and report cell sizes.
minor comments (5)
  1. [Figure 2 and surrounding text] The labels 'Dagumented' and 'D1-agumented' are typos; they should read 'Augmented' and 'D1-augmented'.
  2. [Throughout] The metric name is written inconsistently as 'ROUGEL-F1' (e.g., Table 1, Table 4) and 'ROUGE L-F1' (Section 5.1); please standardize to 'ROUGE-L F1'.
  3. [Throughout] The dataset name is inconsistently capitalized as 'FairyTaleQA' and 'FairytaleQA'; please choose one form and use it consistently.
  4. [Section 5.2] The sentence 'Appendix A shows further support by reporting semantic proximity results' could be clearer as 'reporting BLEURT semantic similarity results'.
  5. [Reproducibility] The paper does not mention whether code or models will be released; for reproducibility, please include an availability statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the DCQG evaluation reuses the same simulated-learner QA systems that produced the difficulty labels, but the paper discloses this as a proxy limitation and the central generation claim is not fitted to the evaluation.

full rationale

The paper's derivation chain is self-contained rather than circular. Difficulty labels are estimated in Sections 4.2-4.4 by fine-tuning five QA systems on SQuAD and applying IRT to their binary responses to FairyTaleQA questions; the generator is then fine-tuned on these labels and evaluated in Section 5.1 by measuring the same QA systems' accuracy on newly generated questions, alongside external narrative similarity metrics (ROUGE-L and BLEURT against human-authored ground truth). The DCQG evaluation does reuse the same five QA systems that produced the difficulty labels, so it is an internal consistency check of an operational proxy rather than independent evidence about human-perceived difficulty. However, this is explicitly acknowledged in the Limitations section: "the evaluation relies on simulated learner responses rather than real student data... it may not fully reflect how actual students would respond." The paper also qualifies its central claim as "preliminary evidence" and reports partial failures at medium and moderate levels, showing that the result is not forced by construction. No equation reduces to another by construction, no fitted parameter is renamed as a prediction, and the self-citations to the authors' prior narrative-control work are motivational and independently corroborated by external ground-truth comparisons. All load-bearing steps are therefore supported by disclosed, operational evidence rather than by circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper has no invented entities. The main uncharged premises are the IRT model itself, the simulated-learner proxy for human ability, and the validity of similarity metrics as evidence of narrative control. Several hand-set thresholds and sampling parameters are free choices that shape the difficulty labels and generated outputs.

free parameters (5)
  • Correctness threshold for QA answers = ROUGE-L-F1 >= 0.5 or exact match
    Hand-set threshold in Section 4.3 that converts QA outputs into the binary response matrix used for all IRT difficulty estimates.
  • Number of simulated learners = 5
    Chosen empirically (Section 4.2) 'to provide sufficient granularity for analysis while avoiding ties'; affects IRT estimation stability.
  • Difficulty binning cut points = 0, 0.28, 0.50, 0.72, 1
    Continuous IRT estimates are normalized and discretized into five categories (Section 4.4); the cut points are not derived from a stated principled rule.
  • Generation sampling hyperparameters = top-k=50, p=0.9, temp=1.2
    Reported as 'obtained experimentally' in Section 4.5; these values affect output diversity and hallucination rate.
  • Early stopping patience = 2 epochs
    Training detail in Section 4.5; chosen without stated search, affects model convergence.
assumptions (5)
  • standard math Rasch one-parameter IRT model (Eq. 1) correctly describes question answering probability
    Section 2.2; standard model but a substantive assumption about how ability and difficulty combine.
  • domain assumption Simulated-learner QA systems approximate human student ability
    Section 4.2 and Limitations; the entire difficulty label and evaluation pipeline depends on this proxy.
  • domain assumption FairytaleQA narrative labels are accurate expert annotations
    Section 2.3; narrative labels are taken as ground truth for training and evaluation.
  • domain assumption ROUGE-L-F1 and BLEURT similarity to human-authored questions measures narrative control
    Section 5.1; inherited from prior work, but similarity is an indirect proxy for controlling the intended narrative category.
  • domain assumption SQuAD-trained QA models transfer to FairyTaleQA stories without bias
    Section 5.2 footnote; the authors intentionally avoid fine-tuning on FairyTaleQA, assuming SQuAD training gives unbiased readers for children's stories.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Advancing Question Generation with Joint Narrative and Difficulty Control." pith.science (2026). https://pith.science/paper/NHLVGPJ2

@misc{pith2026250606812,
  author       = {Pith},
  title        = {Pith review of: Advancing Question Generation with Joint Narrative and Difficulty Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NHLVGPJ2}},
  note         = {Machine review of arXiv:2506.06812}
}
read the original abstract

Question Generation (QG), the task of automatically generating questions from a source input, has seen significant progress in recent years. Difficulty-controllable QG (DCQG) enables control over the difficulty level of generated questions while considering the learner's ability. Additionally, narrative-controllable QG (NCQG) allows control over the narrative aspects embedded in the questions. However, research in QG lacks a focus on combining these two types of control, which is important for generating questions tailored to educational purposes. To address this gap, we propose a strategy for Joint Narrative and Difficulty Control, enabling simultaneous control over these two attributes in the generation of reading comprehension questions. Our evaluation provides preliminary evidence that this approach is feasible, though it is not effective across all instances. Our findings highlight the conditions under which the strategy performs well and discuss the trade-offs associated with its application.

Figures

Figures reproduced from arXiv: 2506.06812 by the authors.

Figure 1
Figure 1. Illustrative example of controlled question [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overall methodology for joint narrative and difficulty control. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Percentage (%) of correct answers by difficulty [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Percentage (%) of correct answers by difficulty [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Percentage (%) of correct answers per narra [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Percentage (%) of correct answers per narra [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Examples of problematic generated question [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Percentage of Correct Answers by Dif. Level. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Proportion of initial interrogative terms in [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 9 canonical work pages

  1. [5]

    arXiv preprint arXiv:2111.09543

    Debertav3: Improving deberta using electra-style pre- training with gradient-disentangled embedding shar- ing. arXiv preprint arXiv:2111.09543. Tomáš Koˇciský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Ed- ward Grefenstette

    arXiv

  2. [6]

    In The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I, page 382–398, Berlin, Hei- delberg

    Difficulty-controllable multi-hop question gen- eration from knowledge graphs. In The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I, page 382–398, Berlin, Hei- delberg. Springer-Verlag. Ghader Kurdi, Jared Leo, Bijan Parsia, Uli Sattler, and Salam Al-Emari

    work page 2019

  3. [7]

    In Findings of the Asso- ciation for Computational Linguistics: ACL 2024 , pages 4715–4729, Bangkok, Thailand

    Planning first, ques- tion second: An LLM-guided method for control- lable question generation. In Findings of the Asso- ciation for Computational Linguistics: ACL 2024 , pages 4715–4729, Bangkok, Thailand. Association for Computational Linguistics. Chin-Yew Lin

    work page 2024

  4. [8]

    arXiv preprint arXiv:1907.11692,

    Roberta: A robustly opti- mized bert pretraining approach. arXiv preprint arXiv:1907.11692,

    arXiv 1907

  5. [10]

    arXiv preprint arXiv:1910.01108

    Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. Thibault Sellam, Dipanjan Das, and Ankur Parikh

    arXiv 1910

  6. [11]

    In Pro- ceedings of the 2022 Conference on Empirical Meth- ods in Natural Language Processing, pages 670–688, Abu Dhabi, United Arab Emirates

    Generative language mod- els for paragraph-level question generation. In Pro- ceedings of the 2022 Conference on Empirical Meth- ods in Natural Language Processing, pages 670–688, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. Masaki Uto, Yuto Tomikawa, and Ayaka Suzuki

    work page 2022

  7. [13]

    why” and “how

    Educa- tional question generation of children storybooks via question type distribution learning and event-centric summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 5073–5085, Dublin, Ireland. A Narrative Control: Semantic Similarity Table 6 presents the results from ...

    work page 2020

  8. [2016]

    In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 2383–2392, Austin, Texas

    SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 2383–2392, Austin, Texas. Association for Computational Linguistics. 11 Vasile Rus, Zhiqiang Cai, and Art Graesser

    work page 2016

Show all 13 references
  1. [2019]

    BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 4171...

    2019

  2. [2021]

    arXiv preprint arXiv:2110.06560

    Simple or complex? complexity-controllable ques- tion generation with soft templates and deep mixture of experts model. arXiv preprint arXiv:2110.06560. David Chen and William Dolan

    arXiv

  3. [2022]

    In Find- ings of the Association for Computational Linguis- tics: ACL 2022, pages 2131–2146, Dublin, Ireland

    Question generation for reading comprehension as- sessment by modeling how and what to ask. In Find- ings of the Association for Computational Linguis- tics: ACL 2022, pages 2131–2146, Dublin, Ireland. Association for Computational Linguistics. Pengcheng He, Jianfeng Gao, and ...

    2022

  4. [2023]

    In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 119–129, Toronto, Canada

    Difficulty-controllable neural question generation for reading comprehension using item response theory. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 119–129, Toronto, Canada. Asso- ciation for Computation...

    2023

  5. [2024]

    In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17351–17370, Miami, Florida, USA

    StorySparkQA: Expert-annotated QA pairs with real-world knowl- edge for children’s story-based learning. In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17351–17370, Miami, Florida, USA. Association for Computational Linguisti...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.