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REVIEW 5 major objections 4 minor 20 references

Steps are all you need: Rethinking STEM Education with Prompt Engineering

T0 review · 5 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that a combined prompt—three chain-of-thought examples plus self-generated analogies—raises Mixtral 8x7B's response-match accuracy on high-school physics and math from a 42% baseline to 66.2%, above its 64.5% K-shot…

desk verdict Plausible prompt combination and a useful new dataset, but the evaluation protocol (retry-on-low-similarity, single human judge, test-set K selection) means the headline 66.2% gain is not reliably supported. read the letter →

arxiv 2412.05023 v3 pith:BKZOO5SX submitted 2024-12-06 cs.CL

Krishnasai Addala , Kabir Dev Paul Baghel , Navya Gupta , Rishitej Reddy Vyalla , Chhavi Kirtani , Avinash Anand , Rajiv Ratn Shah This is my paper · ORCID
classification cs.CL
keywords promptengineeringchain-of-thoughtpromptinganalogicalfew-shotmixtureofexpertsSTEMeducationphysicsandmathematicsquestionansweringStepdataset
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 is trying to establish that open-source language models can be made to reason through high-school physics and mathematics problems by changing the prompt rather than the model. To that end it introduces StemStep, a dataset of 928 step-by-step physics and mathematics questions, and proposes Analogical CoT prompting, which combines a few chain-of-thought examples with the model's self-generated analogies. The key empirical claim is that this combined prompt raises Mixtral 8x7B's response-match accuracy to 66.2%, above the 42% zero-shot baseline and the 64.5% K-shot chain-of-thought result, while smaller Mistral 7B benefits only from the few-shot chain-of-thought part. If the claim holds, it matters because it offers a cheap, training-free route to better STEM tutoring answers from models that otherwise struggle with analogical reasoning.

What carries the argument

The central mechanism is Analogical CoT prompting, a prompt template that shows the model three fully worked example solutions and then instructs it to recall or generate additional analogous examples before solving the target question, so that the model supplies its own few-shot context. The paper argues that this compensates for the weak recall of open-source models that makes pure analogical prompting fail. The other load-bearing element is StemStep, the 928-question dataset of high-school physics and mathematics problems with step-by-step reference solutions that serves as the evaluation ground truth. The Mixture-of-Experts architecture of Mixtral 8x7B, with a router that selects among eight specialized experts, is treated as the reason the combined prompt works on that model but not on Mistral 7B.

What would settle it

Re-run the Mixtral 8x7B comparison on StemStep without the regeneration step, scoring every initial response with the same human-judgment protocol; if the 66.2% Analogical CoT accuracy drops to the 64.5% K-shot CoT level or below, the claimed advantage is an artifact of discarding low-similarity outputs.

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Extended reading notes

Core claim

The paper's central claim is that analogical prompting, which works on large proprietary models, fails on open-source models because those models cannot recall relevant solved examples on their own, and that this failure can be repaired by seeding the prompt with three worked examples before asking the model to generate further examples. On the StemStep dataset, this Analogical CoT prompt lifts Mixtral 8x7B to 66.2% response-match accuracy, compared with 42% under a zero-shot baseline and 64.5% under K-shot chain-of-thought prompting. The authors also report that Mistral 7B does not gain from the combined method (32% versus a 31.5% baseline) but does gain from K-shot CoT (53%), and that the StemStep dataset and the proposed prompt together provide a way to evaluate and improve STEM education assistance without fine-tuning.

Load-bearing premise

The reported accuracy gains assume the model's first response is what is being scored, but the evaluation regenerates any output whose text-similarity score to the reference is below 0.3 and then has a single human judge say whether the final output matches the ground truth, so the numbers could partly reflect a re-roll effect rather than the model's unaided performance.

Editorial extensions

If this is right

  • On Mixtral 8x7B, Analogical CoT reaches 66.2% response-match accuracy, beating the 42% zero-shot baseline and the 64.5% K-shot CoT baseline, so the combined prompt is the strongest prompting method in the paper's comparison.
  • Mistral 7B's analogical performance stays near baseline, so the benefit of the combined prompt is not universal across open-source models and depends on scale or architecture.
  • The proposed prompting style can be applied to any model and dataset without fine-tuning, which makes it a low-cost complement to training-based improvements.
  • Longer or more complex questions gain more from longer reasoning chains, so prompt construction should adapt the number of steps to problem difficulty.
  • StemStep provides a reusable evaluation set of 928 step-by-step high-school physics and mathematics questions for future prompt-engineering and fine-tuning studies.

Reading between the lines

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

  • Beyond the paper: the bottleneck for analogical prompting on small open models may be example recall rather than reasoning, which suggests that adding a lightweight retrieval step—pulling similar solved problems from a small corpus—could give bigger gains than further prompt changes.
  • Beyond the paper: because Mixtral 8x7B is a gated mixture of eight smaller experts, an ablation against a dense model with a similar total parameter count would isolate whether the gain comes from the MoE architecture or from overall scale.
  • Beyond the paper: the same Analogical CoT template should transfer to other step-based STEM subjects, such as chemistry or biology, though the paper only tests physics and mathematics.
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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

5 major / 4 minor

Summary. This paper introduces StemStep, a dataset of 928 high-school physics and mathematics questions with step-by-step solutions, and proposes Analogical CoT prompting, which combines few-shot chain-of-thought examples with self-generated analogical examples. The authors evaluate Mistral 7B and Mixtral 8x7B on StemStep and report that Analogical CoT raises Mixtral 8x7B's response-match accuracy from a 42% baseline to 66.2%, while K-shot CoT reaches 64.5% (Table 1). They also observe that Mistral 7B benefits from few-shot CoT but not from analogical prompting. The central claim is that the proposed combined prompting method allows smaller open-source models to exploit analogical reasoning, and that the StemStep dataset is a useful evaluation resource for STEM education.

Significance. If the headline results were established under a fixed, auditable protocol, the paper would make a useful practical contribution: Analogical CoT is a simple, plausible way to improve open-source models on multi-step STEM problems, and StemStep could complement existing benchmarks such as SciQ, GSM8K, and JEEBench with a longer-form, step-based format. The paper is also honest about some limitations, noting in Section 7 that StemStep's small size (928 questions) limits its utility for fine-tuning and that quantized MoE models degrade in performance. However, the empirical significance is currently undercut by the evaluation methodology: the reported accuracies depend on a response-regeneration rule and a single subjective human evaluator, the few-shot order K is selected from the same test curves that produce the headline numbers, and the dataset is not released. These issues are load-bearing for the paper's main claim, and they are not acknowledged in the Limitations section.

major comments (5)
  1. [Section 5.4 and Appendix] The accuracy metric is not a fixed protocol. Section 5.4 defines Accuracy as a single evaluator's judgment that the model output is 'inline with the ground truth,' and the Appendix states that responses with TF-IDF cosine similarity below 0.3 to the ground truth are regenerated. The paper does not state how many regenerations were permitted per question, how many outputs were regenerated in each condition, or whether the human score was assigned to the first attempt or to the final (possibly regenerated) output. If the Analogical CoT condition produced more low-similarity first attempts that were retried until one passed, the 24-point gap between baseline and Analogical CoT in Table 1 could be an artifact of retry counts rather than prompt quality. Please report the full protocol: the retry cap, regeneration counts per condition, and accuracy computed on first attempts and on final attempts separately, together with confidence intervals or raw counts.
  2. [Table 1 and Section 5.1] The few-shot values K used for the headline comparison appear to be selected from the test set. The text says Mistral 7B performance 'peaks around K=3' and that for Mixtral 8x7B 'K=6 seems to show the most consistent performance,' and Table 1 then reports accuracy at exactly these K values. Selecting K from the same test curves used to report the results is a form of test-set fitting and makes the reported K-shot CoT advantage over baseline optimistically biased. Please select K on a validation split (or report a pre-registered selection rule), and report accuracy for all K values considered, not only the best one.
  3. [Section 3 and Appendix] StemStep is not released, all experiments are conducted on this private dataset, and no external benchmark is used, so the reported results cannot be independently audited. The dataset description is also thin: Section 3 says the data were 'scraped from online resources' without specifying sources, licensing, or quality filters beyond a 5-person survey of 50 questions each, and no inter-annotator agreement is reported for that survey. In addition, the dataset size is given as 928 in Section 3 but as '1000 Physics and Mathematics questions' in the Abstract and Conclusion. Please release the dataset (or a curated sample), provide a detailed datasheet, and reconcile the size discrepancy.
  4. [Section 4.3 and Figure 5] The proposed Analogical CoT prompt fixes the number of supplied examples at 3 and the number of self-generated examples at 3, with no ablation or sensitivity analysis. The paper states that these values were chosen 'on analysis of prior work,' but the cited prior work (Yasunaga et al., 2023) uses pure analogical prompting rather than the combined CoT-plus-analogical prompt. Since the headline gain in Table 1 depends on this specific configuration, the absence of any variation of the number of supplied versus self-generated examples leaves the central claim underdetermined. I would expect at least a small ablation varying (3,3) to (3,0) and (0,3), or a justification from a validation split.
  5. [Section 4] The sentence 'LoRA was employed to mitigate the computational challenges associated with inference' is technically inaccurate as written: LoRA is a parameter-efficient fine-tuning method, not an inference-time technique. If the authors used QLoRA to quantize the models for inference, they should say so explicitly and specify the quantization settings, because Section 7 states that 4-bit quantization degrades Mixtral's performance. This matters because the reported absolute accuracies are for quantized models; a reader cannot currently tell which numbers correspond to which quantization level.
minor comments (4)
  1. [Abstract / Section 3 / Conclusion] The dataset size is inconsistent: the Abstract and Conclusion say 1000 questions, while Section 3 says 928. Please correct the inconsistency.
  2. [Section 4.3] The paper states that the prompt was tested on GPT-4 and Gemini Pro, but no results for these models are reported anywhere in the paper. Either report those results or remove the claim.
  3. [Figures 3-6] Several figures (e.g., Figures 3, 4, and 5) appear to lack axis labels and captions that explain what is being plotted. The text refers to 'metrics recorded' without specifying which metric is shown; please add readable axis labels, legends, and captions.
  4. [Throughout] There are numerous typos and grammatical errors, including 'neccesary', 'the the evaluator', 'succesfully', and 'succesfully'. The paper would benefit from a careful proofreading pass.

Circularity Check

1 steps flagged · score 4.0 of 10

Reported K-shot accuracies are selected maxima from the test curves, but the Analogical CoT claim retains independent content.

  1. fitted input called prediction [Section 4.1 (K Variable K Shot Prompting) and Section 5 (Results, Table 1)]
    "The model did not display a discernible trend as Mistral 7B did, although K=6 seems to show the most consistent performance."

    For Mixtral, K=6 was selected from the test accuracy curves before being reported as the K-Shot CoT Accuracy of 64.5 in Table 1. The companion text for Mistral similarly reports that the model performance peaks around K=3, and Table 1 reports 53 at that selected K. Thus the K-shot accuracies are chosen maxima from the evaluation set, not fixed-protocol predictions; the gain over baseline is partly a test-set selection artifact. The Analogical CoT accuracy (66.2) is less affected because its K=3 is fixed from prior work, so the central claim retains independent content.

full rationale

The central Analogical CoT claim is an empirical comparison, not a derivation that reduces to its inputs: the prompt is a textual combination of existing techniques (CoT, few-shot, analogical prompting), and the 66.2% figure is a measured output under a stated evaluation protocol rather than an algebraic consequence of the protocol. I found one moderate circularity-adjacent step: K for the K-shot CoT conditions was selected from the same test accuracy curves used to report the headline K-shot numbers, making those numbers selected maxima rather than fixed-protocol predictions. The Appendix's regeneration rule (cosine_similarity < 0.3 then regenerate response) and the single human evaluator's 'inline with the ground truth' judgment are serious validity concerns, but they are not definitional circularity: the accuracy judgment is not defined as the retry outcome, and no equation equates the reported result with the filter, so I do not count them as circular steps. The paper contains extensive self-citation (Anand et al.), but those citations support the dataset and related work, not the new prompt combination, and are not load-bearing for the Analogical CoT comparison. Because one reported comparison is fitted to the evaluation data while the central claim retains independent content, the score is 4.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The reported results rest on the StemStep dataset, which is not publicly released, and on evaluation choices (regeneration threshold, K selection, example count) that are either fitted to the test data or unvalidated proxies for correctness. No new theoretical entities are introduced.

free parameters (3)
  • Number of few-shot examples K = K=3 for Mistral 7B, K=6 for Mixtral 8x7B
    Selected after observing accuracy peaks in the test-set curves (Section 5), making the K-shot results partly fit to the evaluation data.
  • Cosine similarity regeneration threshold = 0.3
    Responses below this TF-IDF cosine similarity to the ground truth were regenerated and discarded from scoring (Appendix); threshold set without justification.
  • Number of self-generated analogies in Analogical CoT = 3
    Chosen based on prior work (Yasunaga et al. 2023) rather than fit to data, but still a hand-picked design choice that affects results (Section 4.3).
assumptions (3)
  • domain assumption Scraped online high-school physics and math problems are correct and at the intended difficulty level.
    Section 3: dataset built by scraping; quality checked only by 5 evaluators rating 50 questions each, not by expert verification of all 928.
  • domain assumption Text-similarity metrics and the human 'inline with ground truth' judgment are valid proxies for mathematical correctness.
    Section 5.4: no correlation is shown between ROUGE/METEOR/BERTScore or the human judgment and actual answer correctness; free-form steps may match in wording while being wrong.
  • domain assumption Few-shot prompt examples are drawn from the training split and do not overlap the test questions.
    Appendix dataset preprocessing describes a 60/20/20 split, but the paper never states that prompt examples are disjoint from the evaluation set; leakage would inflate scores.

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Cite this review

Pith. "Pith review of Steps are all you need: Rethinking STEM Education with Prompt Engineering." pith.science (2026). https://pith.science/paper/BKZOO5SX

@misc{pith2026241205023,
  author       = {Pith},
  title        = {Pith review of: Steps are all you need: Rethinking STEM Education with Prompt Engineering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BKZOO5SX}},
  note         = {Machine review of arXiv:2412.05023}
}
read the original abstract

Few shot and Chain-of-Thought prompting have shown promise when applied to Physics Question Answering Tasks, but are limited by the lack of mathematical ability inherent to LLMs, and are prone to hallucination. By utilizing a Mixture of Experts (MoE) Model, along with analogical prompting, we are able to show improved model performance when compared to the baseline on standard LLMs. We also survey the limits of these prompting techniques and the effects they have on model performance. Additionally, we propose Analogical CoT prompting, a prompting technique designed to allow smaller, open source models to leverage Analogical prompting, something they have struggled with, possibly due to a lack of specialist training data.

Figures

Figures reproduced from arXiv: 2412.05023 by the authors.

Figure 1
Figure 1. The Average number of Steps per Question is [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The Average Step Length is 154.9 Characters [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. K Variable K Shot Prompting on Mistral 7B [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: K Variable K Shot Prompting on Mixtral 8x7B [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: CoT+Analogical Prompting on Mixtral 8x7B [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: 3 Shot CoT Prompting on Mistral 7B on a large amount of domain-specific data in order to be able to recall relevant examples. However, as noted in the original work, this method of prompting does hold merit for larger, better trained models such as GPT4. Theoretically,…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.