Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage

Ashok K. Goel; Rahul K. Dass; Sarah Elshabrawy

arxiv: 2606.12767 · v1 · pith:FPIW3Y2Onew · submitted 2026-06-11 · 💻 cs.AI

Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage

Sarah Elshabrawy , Rahul K. Dass , Ashok K. Goel This is my paper

Pith reviewed 2026-06-27 07:28 UTC · model grok-4.3

classification 💻 cs.AI

keywords procedural reasoningquestion generationevaluation datasetsTMK modelsgrounding validationmulti-hop reasoningAI-supported learning

0 comments

The pith

Strict generation from Task-Method-Knowledge models produces the most grounded datasets for procedural reasoning evaluation.

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

The paper examines three strategies for creating question-answer pairs to test AI systems on procedural and multi-hop reasoning in instructional settings. It introduces a validation method that checks whether each question is self-contained and whether its answer is supported by evidence units drawn from structured Task-Method-Knowledge models. Across 23 topics and 690 generated pairs, the strict TMK approach yields 96.5 percent grounded questions and 92.6 percent usable ones, outperforming transcript-first and hybrid methods on grounding while still supporting multi-hop coverage. A sympathetic reader would care because evaluation datasets must reflect the actual knowledge representations that learning systems are meant to use rather than surface-level text patterns.

Core claim

Strict generation from Task-Method-Knowledge (TMK) models achieves the strongest overall quality, with 96.5% grounded questions and 92.6% usable questions across 23 instructional topics and 690 generated pairs. Transcript-first generation produces more learner-like questions but yields more context-dependent or weakly grounded items. TMK-aware generation yields high raw multi-hop coverage but lower grounding. These outcomes demonstrate that procedural richness and natural phrasing do not guarantee representational grounding, motivating explicit representation-aware validation for evaluation datasets.

What carries the argument

The grounding validation framework, which extracts closed-set evidence units from TMK models to measure whether answers are supported by the underlying representation and whether questions are self-contained.

If this is right

Procedural richness obtained from transcripts does not ensure that questions remain grounded in the instructional representation.
Different generation strategies create explicit trade-offs among natural phrasing, grounding, and multi-hop coverage.
Evaluation datasets for AI-supported learning require representation-aware validation rather than relying on surface features alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Datasets built this way could serve as benchmarks for testing whether new procedural reasoners actually operate over the same knowledge structures used in instruction.
The validation framework might transfer to other structured representations such as hierarchical task networks or causal graphs used in planning systems.

Load-bearing premise

The grounding validation framework based on closed-set evidence units extracted from TMK models correctly determines whether answers are supported by the underlying representation and whether questions are self-contained.

What would settle it

Independent human raters assessing the same 690 pairs for grounding and usability and finding agreement with the framework below 80 percent would falsify the claim that the framework reliably measures representational support.

Figures

Figures reproduced from arXiv: 2606.12767 by Ashok K. Goel, Rahul K. Dass, Sarah Elshabrawy.

**Figure 2.** Figure 2: Quality and multi-hop coverage by generation strategy. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

read the original abstract

Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use. We study how TMK-based question generation strategies affect dataset quality for procedural and multi-hop reasoning. We compare three strategies: strict generation from Task-Method-Knowledge (TMK) models, transcript-first generation with post-hoc TMK filtering, and TMK-aware generation that combines transcripts with structured guidance. To evaluate generated items, we introduce a grounding validation framework based on closed-set evidence units extracted from TMK models. The framework measures whether answers are supported by the underlying representation, whether questions are self-contained, and whether they target multi-hop procedural reasoning. Across 23 instructional topics and 690 generated question-answer pairs, strict TMK generation achieves the strongest overall quality, with 96.5% grounded questions and 92.6% usable questions. Transcript-first generation produces more learner-like questions but more context-dependent or weakly grounded items, while TMK-aware generation yields high raw multi-hop coverage but lower grounding. These results show that procedural richness and natural phrasing do not guarantee representational grounding, motivating explicit representation-aware validation for evaluation datasets in AI-supported learning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's three-way comparison on 690 items is useful but the TMK-derived evidence units likely stack the grounding metric toward the strict generation strategy.

read the letter

The main thing here is a head-to-head test of three TMK-based ways to build procedural QA datasets: strict generation from the model, transcript-first with later filtering, and TMK-aware mixing of both. On 23 topics they produced 690 pairs and scored them with a new closed-set evidence-unit framework that checks grounding, self-containment, and multi-hop coverage. Strict TMK came out ahead at 96.5% grounded and 92.6% usable, while the other two traded naturalness or multi-hop coverage for weaker grounding scores.

The concrete comparison on fixed topics and the validation framework are the real additions. Holding the source material constant lets them show that natural phrasing or high multi-hop counts do not automatically produce items that stay faithful to the underlying representation. That trade-off result is worth having for anyone building evaluation sets in AI-supported learning.

The soft spot is the validation step itself. Evidence units are extracted from the TMK models, so items generated directly from those models will match the units by design. Transcript-based items can be perfectly valid for the original instruction yet score lower simply because they diverge from the TMK units. Without an external check such as human experts or a separate knowledge source, the claim that strict TMK is strongest risks measuring fidelity to the model rather than true grounding. The abstract also leaves out error bars, an exact definition of usable, how multi-hop coverage was tallied, and any inter-rater numbers.

This is for researchers who construct or evaluate datasets for procedural reasoning in education technology. Readers already working with TMK or similar structured representations will get the most out of the numbers and the framework. It is a practical methods paper rather than a theoretical one.

I would send it for peer review. The empirical design is clear enough that referees can examine the counting procedures and ask whether the framework can be made independent of the generation method.

Referee Report

2 major / 1 minor

Summary. The paper claims that for constructing evaluation datasets for procedural reasoning, three TMK-based generation strategies were compared across 23 instructional topics yielding 690 question-answer pairs. Strict TMK generation from Task-Method-Knowledge models produces the highest quality with 96.5% grounded questions and 92.6% usable questions according to a new grounding validation framework that extracts closed-set evidence units from TMK models to assess support for answers, self-containment of questions, and multi-hop procedural targeting. Transcript-first generation yields more learner-like items but weaker grounding, while TMK-aware generation shows high multi-hop coverage but lower grounding scores. The work concludes that naturalness and procedural richness do not guarantee representational grounding, motivating explicit representation-aware validation.

Significance. If the central comparative result holds after addressing validation concerns, the work is significant for AI-supported learning systems because it supplies concrete empirical evidence that procedural dataset quality requires explicit grounding checks rather than relying on natural phrasing or multi-hop coverage alone. The study provides a reproducible empirical comparison using explicitly defined metrics on generated items and introduces a validation framework that could be extended to other structured representations.

major comments (2)

[Grounding validation framework (abstract and methods)] The grounding validation framework (described in the abstract and the methods) extracts closed-set evidence units from TMK models to determine whether answers are supported and questions are self-contained. Strict TMK generation produces items directly from the same TMK representation, so the reported 96.5% grounded rate is expected by construction; transcript-first and TMK-aware strategies start from external transcripts and are scored against the same units, creating a risk that mismatches reflect divergence from the TMK model rather than true lack of grounding in the instructional material. This is load-bearing for the claim that strict TMK achieves the strongest overall quality.
[Results reporting (abstract and results section)] The abstract reports concrete percentages (96.5% grounded, 92.6% usable) across 690 pairs but supplies no error bars, no explicit definition of 'usable', no description of how multi-hop coverage was counted, no details on the 23 topics, and no inter-rater reliability statistics for the validation. These omissions prevent assessment of the stability and replicability of the metrics that underpin the main comparative conclusion.

minor comments (1)

[Abstract] The abstract states that the framework measures 'whether they target multi-hop procedural reasoning' but does not indicate the operational criteria or counting procedure used for this dimension.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and indicate planned revisions.

read point-by-point responses

Referee: [Grounding validation framework (abstract and methods)] The grounding validation framework (described in the abstract and the methods) extracts closed-set evidence units from TMK models to determine whether answers are supported and questions are self-contained. Strict TMK generation produces items directly from the same TMK representation, so the reported 96.5% grounded rate is expected by construction; transcript-first and TMK-aware strategies start from external transcripts and are scored against the same units, creating a risk that mismatches reflect divergence from the TMK model rather than true lack of grounding in the instructional material. This is load-bearing for the claim that strict TMK achieves the strongest overall quality.

Authors: The framework is intentionally representation-aware and measures alignment to the TMK model as the target knowledge structure for the AI-supported learning system. While strict generation scores high by construction, the comparative results show that transcript-based methods yield lower alignment even when drawn from the same instructional topics. This supports the paper's conclusion that naturalness and multi-hop coverage do not ensure grounding. We will revise the methods section to explicitly discuss the framework's design and this interpretive point. revision: partial
Referee: [Results reporting (abstract and results section)] The abstract reports concrete percentages (96.5% grounded, 92.6% usable) across 690 pairs but supplies no error bars, no explicit definition of 'usable', no description of how multi-hop coverage was counted, no details on the 23 topics, and no inter-rater reliability statistics for the validation. These omissions prevent assessment of the stability and replicability of the metrics that underpin the main comparative conclusion.

Authors: We agree that these details are needed for replicability. In the revised manuscript we will add error bars to the reported percentages, an explicit definition of 'usable', a description of the multi-hop counting procedure, additional details on the 23 topics, and inter-rater reliability statistics, updating both the abstract and results section accordingly. revision: yes

Circularity Check

1 steps flagged

Grounding validation defined from TMK evidence units makes strict TMK scores tautological by construction

specific steps

self definitional [Abstract]
"To evaluate generated items, we introduce a grounding validation framework based on closed-set evidence units extracted from TMK models. The framework measures whether answers are supported by the underlying representation, whether questions are self-contained, and whether they target multi-hop procedural reasoning. Across 23 instructional topics and 690 generated question-answer pairs, strict TMK generation achieves the strongest overall quality, with 96.5% grounded questions and 92.6% usable questions."

Groundedness is defined as support by evidence units extracted from TMK models. Strict TMK generation produces items directly from those same TMK models, so the reported 96.5% grounded rate follows by construction from the shared source rather than from an independent test of grounding.

full rationale

The paper's main empirical claim (strict TMK generation yields 96.5% grounded / 92.6% usable items) rests on a validation framework whose closed-set evidence units are extracted directly from the same TMK models used to generate the strict-TMK items. This produces an alignment score that is definitionally expected rather than independently tested. Other generation strategies are scored against the identical TMK-derived units, creating an asymmetric and self-referential metric. No external grounding oracle or human judgment independent of the TMK representation is described. The result therefore reduces to a comparison of fidelity to the TMK source rather than an external measure of representational grounding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that TMK models constitute complete and accurate representations of the instructional content and that the new validation framework faithfully measures grounding without circular dependence on the generation process itself. No free parameters or invented physical entities are introduced.

axioms (1)

domain assumption TMK models extracted from instructional materials provide a sufficient closed-set representation for validating answer support and question self-containment.
Invoked when the grounding validation framework is defined and applied to score the generated items.

pith-pipeline@v0.9.1-grok · 5756 in / 1364 out tokens · 20288 ms · 2026-06-27T07:28:01.382077+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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URLhttps://aclanthology.org/2023.cl-4.2/

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G.S., Turc, I., Reitter, D.: Measuring attribution in natural language generation models. Computational Linguistics49(4), 777–840 (Dec 2023).https: //doi.org/10.1162/coli_a_00486,https://aclanthology.org/2023.cl-4.2/

work page doi:10.1162/coli_a_00486 2023

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Musique: Multihop questions via single-hop question composition.Trans

Trivedi, H., Balasubramanian, N., Khot, T., Sabharwal, A.: MuSiQue: Multihop questions via single-hop question composition. Transactions of the Association for Computational Linguistics10, 539–554 (2022).https://doi.org/10.1162/tacl _a_00475,https://aclanthology.org/2022.tacl-1.31/

work page internal anchor Pith review doi:10.1162/tacl 2022

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Vanlehn, K.: The behavior of tutoring systems. Int. J. Artif. Intell. Ed.16(3), 227–265 (Aug 2006)

2006

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shortcut

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., Manning, C.D.: HotpotQA: A dataset for diverse, explainable multi-hop question answering. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 2369–2380. Association for Computational ...

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2025