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REVIEW 3 major objections 3 minor 30 references

Ask Good Questions for Large Language Models

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Concept-aware item response theory could tell an LLM tutor what to ask next.

desk verdict A plausible new blend of psychometric modeling and LLM question generation, but the abstract is pure promise and the full text is corrupted, so I can't tell if the experiments are real. read the letter →

arxiv 2508.14025 v1 pith:D3ODPWLS submitted 2025-08-19 cs.CL cs.AI

Qi Wu , Zhongqi Lu This is my paper
classification cs.CLcs.AI
keywords questiongenerationitemresponsetheoryconceptmodelingLLMdialoguesystemsknowledgeestimationguidingquestionsinformationretrievaltutoring
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 argues that dialog systems fail as tutors because they cannot tell which concept a user is actually confused about, so their follow-up questions often miss the point. To fix this, it introduces the Ask-Good-Question framework, built around an upgraded Concept-Enhanced Item Response Theory (CEIRT) model that estimates a user's knowledge at the level of individual concepts. Those estimates are fed to an LLM, which generates a guiding question aimed at the user's specific gap. The paper claims that this combination, compared with existing dialog baselines, speeds up and improves the question-answer retrieval process. A sympathetic reader would care because targeted questioning is the difference between a dialog system that merely answers and one that actually helps a learner find what they are missing.

What carries the argument

The load-bearing mechanism is the Concept-Enhanced Item Response Theory (CEIRT) model, an extension of item response theory that estimates a user's knowledge level per concept rather than as a single global ability score. In item response theory, the probability of a correct answer is modeled as a function of user ability and item difficulty; CEIRT adds concept-level structure so that the model can attribute a wrong answer to a specific conceptual gap. That per-concept estimate is then the conditioning signal the LLM uses to generate the next guiding question.

What would settle it

Run an experiment where users are given a concept-level pretest, then interact with the AGQ system; if CEIRT's estimated concept weakness does not match the pretest-confirmed confusion, or if questions aimed at that concept do not resolve the user's gap faster than generic questions, the central claim loses its support.

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Extended reading notes

Core claim

The central claim is that applying CEIRT for concept-level knowledge estimation, together with an LLM that generates guiding questions from the user's current text, produces measurably better information retrieval experiences in dialog than baseline methods. The proposed pipeline treats each user response as evidence about which linked concepts are mastered and which are confused, then turns that estimate into a next question. If the claim is right, the framework would let a dialog system guide a user toward the right information in fewer steps, because it targets the concept behind the user's confusion rather than the literal words.

Load-bearing premise

The framework only works if user responses in a dialog can be scored accurately enough by item response theory to identify the specific concept the user actually confuses.

Editorial extensions

If this is right

  • If the framework is correct, dialogue systems can move from reactive answering to active tutoring by scoring each user answer as evidence of a concept-level gap.
  • The integration of IRT-style estimates with LLM generation suggests a modular recipe: a psychometric scorer picks the weak concept, a language model phrases the question, and the loop repeats.
  • Users searching or learning through conversation could reach the relevant information in fewer exchanges because each follow-up question is aimed at the diagnosed confusion.
  • The paper's comparison with baseline methods implies that simply asking an LLM to continue the conversation is not enough; the concept-level estimate is what makes the next question useful.

Reading between the lines

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

  • A natural extension the paper does not develop: CEIRT estimates could be validated directly against a user's self-reported confusion or post-session test scores, isolating whether the estimate or the question generator drives the improvement.
  • The same scoring-plus-generation loop could transfer to educational chatbots, FAQ navigation, and technical support ticketing, wherever a wrong answer masks a specific missing concept.
  • One testable prediction is that question quality degrades more sharply when concept labels are removed from the IRT model than when the LLM is replaced, which would show where the framework's value lies.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The paper introduces the Ask-Good-Question (AGQ) framework, which combines a Concept-Enhanced Item Response Theory (CEIRT) model with large language models to generate guiding questions in dialog systems. The abstract claims that CEIRT 'better identifies users' knowledge levels' and that the approach 'outperforms by significantly enhencing the users' information retrieval experiences.' The supplied full text is almost entirely encoding-corrupted, so the model definitions, derivation, and experiments cannot be read. The abstract contains no quantitative evidence: no dataset, baselines, metrics, error bars, or test protocol. The central claim is therefore unsupported as written, and the corrupted body prevents any independent verification.

Significance. If the mechanism works, the paper would address a real and timely problem: using latent knowledge-state estimates to guide LLM-generated questions for more effective information retrieval. The conceptual combination of IRT-style ability estimation with concept-level diagnosis and LLM question generation is plausible and potentially interesting. However, the manuscript as provided contains no machine-checked proofs, no reproducible code, no numerical comparisons, and no readability checkable experiments. The significance is conditional on evidence that is not present in the submitted text.

major comments (3)
  1. [Abstract] The central performance claim, 'our approach outperforms by significantly enhencing the users' information retrieval experiences,' is reported with no supporting numbers. The abstract names no dataset, no baseline methods, no evaluation metric, and no error bars or significance tests. Since the full text is corrupted and cannot be used to fill this gap, the paper's main claim is unsupported as stated. The authors must supply concrete quantitative comparisons with baselines, including effect sizes and uncertainty estimates.
  2. [Full text (Sections 1–5, including equations and tables)] The supplied full text is largely unreadable due to encoding corruption. Equations around the CEIRT model definition and the experimental section are garbled, and the table contents cannot be parsed. It is therefore impossible to verify the derivation of the Concept-Enhanced Item Response Theory model, the question-generation procedure, or the reported comparisons. A manuscript whose body cannot be read cannot support the abstract's claims; the authors need to provide a clean, readable version before further review.
  3. [CEIRT model and evaluation (Sections 3–4)] The AGQ mechanism depends on CEIRT producing accurate concept-level knowledge estimates from users' dialog responses. No calibration evidence is visible: there is no comparison of estimated versus actual concept mastery, no analysis of item difficulty/ability identifiability, and no ablation isolating CEIRT's contribution from the LLM component. If distinct concepts are collapsed or if item difficulty is conflated with user ability, the generated questions will target the wrong concept and the claimed retrieval-efficiency gains will not follow. This is a load-bearing internal-validity requirement that must be addressed with explicit experiments.
minor comments (3)
  1. [Abstract] Typo: 'enhencing' should be 'enhancing.' Also, the hyphenation of 'Ask-Good-Question' is inconsistent with the acronym AGQ.
  2. [Full text] There appear to be repeated blocks of text in the corrupted rendering, for example the passage beginning '�� ����������� ��� ����������� �������� �������'. These should be cleaned or explained.
  3. [Full text] If the garbled text is not a pipeline artifact, the manuscript is missing all legible section headings, equation numbers, and table/figure captions; this alone prevents standard review.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identifiable from the available text; the claimed chain is an empirical pipeline with external comparison.

full rationale

The only fully readable portion of the paper is the abstract. The claimed derivation chain is: (1) CEIRT estimates users' knowledge levels from dialog responses; (2) LLMs generate guiding questions using those estimates and the inspiring text; (3) retrieval efficiency in the Q&A process is compared with baseline methods. None of these steps is, on the available evidence, defined in terms of the others. The abstract does not report fitting a parameter and then predicting the same fitted quantity; it does not invoke a self-citation as the load-bearing justification; it does not rename a known result under new coordinates. The concern that CEIRT's concept-level estimates could be miscalibrated is a substantive empirical-validity question, not an identity between input and output; without the actual equations or evaluation protocol—which the supplied encoding-corrupted body does not allow one to verify—hard rule 1 forbids flagging circularity on speculation. The paper appears to claim an empirical improvement over baselines, which is the kind of externally falsifiable claim that is not circular on its face. If the omitted evaluation reused CEIRT's own estimates as the success metric, that would be circular, but no such reduction is exhibited. Score 0.

Assumptions & free parameters 1 free parameters · 2 assumptions · 2 invented entities

Everything the central claim rests on, as visible from the abstract, is the assumption that IRT-style scoring transfers to dialog data and that LLM questions derived from those scores improve retrieval. No external benchmarks or independent falsifiable handles are presented in the abstract.

free parameters (1)
  • CEIRT model parameters (item difficulty, discrimination, and concept-level knowledge weights)
    The abstract states CEIRT is 'improved' and used to 'identify users' knowledge levels,' which requires fitting model parameters to response data. No values are given, and whether these parameters are fit on the same data used for evaluation cannot be checked from the abstract.
assumptions (2)
  • domain assumption Item response theory can be applied to dialog interaction data to yield valid estimates of user knowledge states.
    The framework's core inference step rests on this; stated implicitly in the abstract's claim that CEIRT 'better identifies users' knowledge levels.'
  • domain assumption Guiding questions generated by an LLM from CEIRT estimates improve information retrieval efficiency.
    This is the causal bridge from knowledge estimation to the claimed outcome; without it, the performance claim collapses. Introduced in the abstract's contribution statement.
invented entities (2)
  • CEIRT (Concept-Enhanced Item Response Theory) model
    purpose: Estimate user knowledge levels and inform question generation.
    The abstract introduces an 'improved' version of CEIRT but gives no formulation, no external benchmark, and no falsifiable prediction that could test the model outside this paper.
  • AGQ (Ask-Good-Question) framework
    purpose: Pipeline that combines CEIRT estimates with LLM question generation for dialog systems.
    Introduced in the abstract; its stated advantage is only supported by the paper's own reported comparisons, which are not quantified in the abstract.

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Cite this review

Pith. "Pith review of Ask Good Questions for Large Language Models." pith.science (2026). https://pith.science/paper/D3ODPWLS

@misc{pith2026250814025,
  author       = {Pith},
  title        = {Pith review of: Ask Good Questions for Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D3ODPWLS}},
  note         = {Machine review of arXiv:2508.14025}
}
read the original abstract

Recent advances in large language models (LLMs) have significantly improved the performance of dialog systems, yet current approaches often fail to provide accurate guidance of topic due to their inability to discern user confusion in related concepts. To address this, we introduce the Ask-Good-Question (AGQ) framework, which features an improved Concept-Enhanced Item Response Theory (CEIRT) model to better identify users' knowledge levels. Our contributions include applying the CEIRT model along with LLMs to directly generate guiding questions based on the inspiring text, greatly improving information retrieval efficiency during the question & answer process. Through comparisons with other baseline methods, our approach outperforms by significantly enhencing the users' information retrieval experiences.

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Works this paper leans on

30 extracted references · 17 canonical work pages

  1. [1]

    S. Back, A. Kedia, S. C. Chinthakindi, H. Lee, and J. Choo. Learning to generate questions by learning to recover answer-containing sentences. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1516--1529, 2021

    work page 2021

  2. [2]

    Chang and Z

    H.-H. Chang and Z. Ying. A-stratified multistage computerized adaptive testing. Applied psychological measurement, 23 0 (3): 0 211--222, 1999

    work page 1999

  3. [3]

    Curto, A

    S. Curto, A. C. Mendes, and L. Coheur. Exploring linguistically-rich patterns for question generation. In Proceedings of the UCNLG+ eval: Language Generation and Evaluation Workshop, pages 33--38, 2011

    work page 2011

  4. [4]

    De La Torre

    J. De La Torre. The generalized dina model framework. Psychometrika, 76 0 (2): 0 179--199, 2011

    work page 2011

  5. [5]

    X. Du, J. Shao, and C. Cardie. Learning to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106, 2017

    arXiv 2017

  6. [6]

    T. GLM, . A. Zeng, and B. X. et.al. Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024. URL https://arxiv.org/abs/2406.12793

    arXiv 2024

  7. [7]

    K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang. Realm: Retrieval-augmented language model pre-training, 2020. URL https://arxiv.org/abs/2002.08909

    arXiv 2020

  8. [8]

    Hernandez, B

    E. Hernandez, B. Z. Li, and J. Andreas. Inspecting and editing knowledge representations in language models, 2024. URL https://arxiv.org/abs/2304.00740

    arXiv 2024

Show all 30 references
  1. [9]

    Labutov, S

    I. Labutov, S. Basu, and L. Vanderwende. Deep questions without deep understanding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pag...

    2015

  2. [10]

    K. Li, O. Patel, F. Viégas, H. Pfister, and M. Wattenberg. Inference-time intervention: Eliciting truthful answers from a language model, 2024. URL https://arxiv.org/abs/2306.03341

    2024 arXiv

  3. [11]

    C.-Y. Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74--81, 2004

    2004

  4. [12]

    Lindberg, F

    D. Lindberg, F. Popowich, J. Nesbit, and P. Winne. Generating natural language questions to support learning on-line. In Proceedings of the 14th European workshop on natural language generation, pages 105--114, 2013

    2013

  5. [13]

    F. M. Lord. Applications of item response theory to practical testing problems. Routledge, 2012

    2012

  6. [14]

    Jaech, and A

    OpenAI, :, A. Jaech, and A. K. et.al. Openai o1 system card, 2024. URL https://arxiv.org/abs/2412.16720

    2024 arXiv

  7. [15]

    Papineni, S

    K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311--318, 2002

    2002

  8. [16]

    Qwen, :, A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, H. Lin, J. Yang, J. Tu, J. Zhang, J. Yang, J. Yang, J. Zhou, J. Lin, K. Dang, K. Lu, K. Bao, K. Yang, L. Yu, M. Li, M. Xue, P. Zhang, Q. Zhu, R. Men, R. Lin, T. Li, T. Tang, T. Xia, ...

    2025 arXiv

  9. [17]

    R. Ren, Y. Wang, Y. Qu, W. X. Zhao, J. Liu, H. Tian, H. Wu, J.-R. Wen, and H. Wang. Investigating the factual knowledge boundary of large language models with retrieval augmentation, 2023. URL https://arxiv.org/abs/2307.11019

    2023 arXiv

  10. [18]

    Scialom, B

    T. Scialom, B. Piwowarski, and J. Staiano. Self-attention architectures for answer-agnostic neural question generation. In Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pages 6027--6032, 2019

    2019

  11. [19]

    D. O. Segall. Multidimensional adaptive testing. Psychometrika, 61 0 (2): 0 331--354, 1996

    1996

  12. [20]

    Shakeri, C

    S. Shakeri, C. N. d. Santos, H. Zhu, P. Ng, F. Nan, Z. Wang, R. Nallapati, and B. Xiang. End-to-end synthetic data generation for domain adaptation of question answering systems. arXiv preprint arXiv:2010.06028, 2020

    2010 arXiv

  13. [21]

    K. K. Tatsuoka. Rule space: An approach for dealing with misconceptions based on item response theory. Journal of educational measurement, pages 345--354, 1983

    1983

  14. [22]

    M. Uto, Y. Tomikawa, and A. Suzuki. 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, 2023

    2023

  15. [23]

    W. J. Van Der Linden and L. M. Reese. A model for optimal constrained adaptive testing. Applied Psychological Measurement, 22 0 (3): 0 259--270, 1998

    1998

  16. [24]

    Vanderwende

    L. Vanderwende. Answering and questioning for machine reading. In AAAI spring symposium: machine reading, volume 91, 2007

    2007

  17. [25]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, . Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  18. [26]

    D. J. Weiss and G. G. Kingsbury. Application of computerized adaptive testing to educational problems. Journal of educational measurement, 21 0 (4): 0 361--375, 1984

    1984

  19. [27]

    Z. Yin, Q. Sun, Q. Guo, J. Wu, X. Qiu, and X. Huang. Do large language models know what they don't know?, 2023. URL https://arxiv.org/abs/2305.18153

    2023 arXiv

  20. [28]

    Zhang, S

    H. Zhang, S. Diao, Y. Lin, Y. R. Fung, Q. Lian, X. Wang, Y. Chen, H. Ji, and T. Zhang. R-tuning: Instructing large language models to say `i don't know', 2024 a . URL https://arxiv.org/abs/2311.09677

    2024 arXiv

  21. [29]

    Zhang, Y

    L. Zhang, Y. Liu, Y. Luo, F. Gao, and J. Gu. Qwen-ig: A qwen-based instruction generation model for llm fine-tuning. In Proceedings of the 2024 13th International Conference on Computing and Pattern Recognition, pages 295--302, 2024 b

    2024

  22. [30]

    Y. Zhu, H. Yuan, S. Wang, J. Liu, W. Liu, C. Deng, H. Chen, Z. Liu, Z. Dou, and J.-R. Wen. Large language models for information retrieval: A survey, 2024. URL https://arxiv.org/abs/2308.07107

    2024

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Reviewed August 5, 2026 · model on record in the stance chip above.