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REVIEW 4 major objections 4 minor 13 references

More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Increasing prompt specificity consistently improves LLM code-generation pass@1, with up to a 0.30 absolute gain on ParEval-OpenMP — evidence that a large part of the benchmark gap is prompt leanness rather than missing domain knowledge.

desk verdict Useful prompt-ladder framework; headline gain is anchored by a solution-leaking ceiling prompt, so the specificity claim needs rework before the numbers are trusted. read the letter →

arxiv 2508.03678 v1 pith:JPDBA4CI submitted 2025-08-05 cs.CL cs.LGcs.PL

Yangtian Zi , Harshitha Menon , Arjun Guha This is my paper · ORCID
classification cs.CLcs.LGcs.PL
keywords promptspecificitycodegenerationpass@1LLMevaluationPartialOrderEvalHumanParparallelprogramming
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

State-of-the-art LLMs ace general coding benchmarks like HumanEval but score far lower on specialized suites such as ParEval, and it has been unclear whether the shortfall is missing domain knowledge or insufficient prompt detail. This paper argues for the second explanation and builds PartialOrderEval, a framework that replaces each benchmark prompt with a partial order of prompts from a bare signature to a maximally detailed, step-by-step specification. Across HumanEval, ParEval-Serial, and ParEval-OpenMP, with Llama-3.x and Qwen2.5-Coder models, pass@1 rises steadily as prompt detail grows, with the largest gains — up to 0.30 absolute — on the hardest parallel subset. If the paper is right, a substantial portion of the measured ParEval gap is prompt leanness, not missing knowledge, and single-prompt pass@1 scores should be supplemented with prompt-specificity curves.

What carries the argument

The load-bearing mechanism is the partial order of prompts generated by PartialOrderEval, anchored by a maximally detailed prompt ptop and a signature-only minimal prompt pbot. From ptop, three augmentation strategies produce intermediate prompts: LLM summarization at word limits of 10, 25, 50, 75, 100, 150, and 200 words; paragraph sampling at retention ratios of 0.2–0.8; and sentence-block masking at mask ratios of 0.2–0.8, each with four random placements. The detail metric D — word count, retention ratio, or inverse mask ratio — orders these prompts, and the evaluation records pass@1 as a function of D, producing performance curves. Construction of ptop is itself a key step: GPT-4.1 drafts a stepwise description of the reference solution, and the authors manually edit or regenerate it until Qwen2.5-Coder-14B reaches pass@1 ≥ 0.8, which fixes the right endpoint of every curve.

What would settle it

Run the same PartialOrderEval suite with ptop produced by a fixed, untuned prompt-generation procedure (no pass@1 feedback loop) on ParEval-OpenMP, and check whether the maximally detailed prompt still yields roughly the reported 0.30 absolute gain over the original prompt; if the gain largely disappears, the headline effect is an artifact of threshold tuning rather than prompt specificity. A complementary check replaces the word-count detail metric with an information-theoretic measure of prompt content and tests whether pass@1 remains monotone in that ordering.

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Extended reading notes

Core claim

The paper's central claim, stated sympathetically, is that LLM code-generation performance is a systematic function of prompt specificity, and that this function can be measured by ordering prompts from minimal to maximally detailed. Using its PartialOrderEval framework, the paper reports that pass@1 monotonically increases with prompt detail across all three benchmark variants and all eight instruction-tuned models tested, that the increase is steeper for ParEval than for HumanEval, and that at the maximally detailed prompt models exceed their original ParEval scores by up to 0.30 (e.g., Qwen2.5-Coder-14B rises from 0.667 to 0.967 on ParEval-OpenMP). The paper interprets this as evidence that ParEval tasks are more challenging and more sensitive to prompt specificity than HumanEval, that larger models need less prompt detail to reach a given accuracy, and that prompt-specificity curves differentiate models more sharply than a single pass@1 number.

Load-bearing premise

The load-bearing premise is that the maximally detailed prompt ptop — the right endpoint of every performance curve — is a fair ceiling for prompt detail, but it is generated by GPT-4.1 and manually edited until Qwen2.5-Coder-14B passes at least 80 percent of tests, so that threshold and tuning model directly set the measured gains; the paper also assumes its detail metric (word count, retention ratio, mask ratio) orders prompts by genuine information content without validating that monotonicity.

Editorial extensions

If this is right

  • A large share of the ParEval gap is prompt-detail rather than domain-knowledge-limited: with maximally detailed prompts, Qwen2.5-Coder-14B reaches 0.983 on ParEval-Serial and 0.967 on ParEval-OpenMP, versus 0.800 and 0.667 on the original prompts.
  • Benchmarks that aim to compare model capability should report performance across prompt specificity, since a single-prompt pass@1 conflates model ability with prompt quality.
  • Specialized suites like ParEval are stronger discriminators of model capability than HumanEval, because the large-versus-small model gap widens with task difficulty and detail sensitivity.
  • Prompt engineers should prioritize explicit input/output specifications, edge-case handling, and stepwise implementation breakdowns, the themes that grow most as prompts lengthen and correlate with accuracy gains.
  • Diminishing returns are real: HumanEval curves plateau around 100 words of detail, and over-verbose prompts can slightly reduce accuracy.

Reading between the lines

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

  • If the effect generalizes, benchmark difficulty is partly a property of prompt style, so cross-benchmark model comparisons are only meaningful once prompt specificity is controlled; this could change how leaderboards are read.
  • The 0.30 figure is a ceiling set by tuning ptop against one model (Qwen2.5-Coder-14B) at a pass@1 threshold of 0.8; other models or an untuned ptop would likely show smaller or different gains, so the number should be treated as a model-specific bound until reproduced.
  • A direct extension would construct ptop by tuning a different model (e.g., Llama-3.3-70B) and compare the resulting curves; divergence would reveal how much of the specificity effect is a property of the prompt rather than the model.
  • The partial-order construction is language-agnostic, so applying it to low-resource languages, SQL, or shell tasks — where benchmark prompts are typically sparse — is a natural test of whether the specificity effect is universal.
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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

4 major / 4 minor

Summary. The paper introduces PARTIALORDEREVAL, a framework that augments code-generation benchmarks with a partially ordered set of prompts spanning from minimal to maximally detailed descriptions, and applies it to HumanEval, ParEval-Serial, and ParEval-OpenMP. Maximally detailed prompts ptop are generated by GPT-4.1 from the original prompt plus the reference solution and manually edited or regenerated until Qwen2.5-Coder-14B reaches pass@1 ≥ 0.8; three families of less detailed variants (LLM summarization at word limits, paragraph sampling, and sentence-block masking) are derived from ptop. The authors report pass@1 curves for four Qwen and four Llama models, find that performance generally rises with prompt detail, with up to a 0.30 absolute gain on ParEval-OMP, and use LLM-based taxonomy annotation to identify promising prompt-detail themes.

Significance. If the result holds, the paper is significant because it complicates the interpretation that ParEval underperformance reflects missing domain knowledge alone, and it contributes a reusable methodology and dataset for prompt-sensitivity evaluation. The paper is also unusually transparent about experimental budgets, and the partial-order prompt generation is a concrete artifact that others can build on. However, the significance is substantially tempered by the construction of ptop from the reference solution and its selection on the same model whose gains are reported; this makes the magnitude of the reported gains not a clean measure of prompt specificity. The qualitative theme analysis is correlational, and the performance curves lack uncertainty quantification.

major comments (4)
  1. [§3.2–3.3 and §4.3] The right endpoint of every performance curve is selected on the outcome being measured. In §3.3, ptop is generated by GPT-4.1 from the original prompt plus the reference solution (Appendix B), then manually edited or regenerated until Qwen2.5-Coder-14B reaches pass@1 ≥ 0.8, with τ = 0.8 imposed by the formal condition in §3.2. Since ptop is the anchor for all three augmentation families and the source of the 0.30 absolute ParEval-OMP gain reported in §4.3, the rise from pbot to ptop is partly by construction. Appendix E makes the confound concrete: the ptop example for ifft states the exact algorithm from the reference solution (conjugate, forward FFT, conjugate, scale by 1/N), which is a solution sketch rather than a more precise task specification. The manuscript should either bound the confound (e.g., construct ptop without access to the reference solution, or ablate prompts that contain step-by-step solution structure), or reframe the claims as measuring 'how much of the gold solution can be recovered from summaries of ptop.' As written, the central attribution of the gains to prompt specificity rather than to domain knowledge inserted into the prompt is not established.
  2. [§4.1–4.3 and Figure 3] The performance curves are computed from a single greedy decoding per prompt (Appendix A.1, temperature = 0), and no error bars, bootstrap intervals, or significance tests are reported. With 60 problems per ParEval subset, a 0.05 absolute difference in pass@1 is within the noise of a binomial proportion (standard error ≈ 0.06), so several intermediate-level differences in Figures 3 and 4 may not be reliable. More importantly, the paper's own Figure 3 shows a HumanEval decline beyond L=200 (e.g., Qwen2.5-Coder-14B at 0.921 for L=200 vs 0.860 at ptop), which contradicts the conclusion's statement that increased prompt specificity 'consistently improves pass@1.' The monotonicity claim should be replaced by a qualified statement, and confidence intervals or per-problem breakdowns should be provided.
  3. [§3.2 and Appendix E] The detail metric D is defined only syntactically (word limit, retention ratio, masking ratio), yet the partial order pbot <D p <D ptop is used to interpret pass@1 as a function of 'specificity.' The manuscript does not validate that these proxies are monotonic in information content. Appendix E shows that paragraph sampling at rp=0.2 can retain the later solution steps for ifft while dropping the earlier ones, while rp=0.8 retains all four steps; a 200-word LLM summary can omit a crucial constraint that a shorter summary includes. Section 3.2 states 'as long as the metric D is reasonably defined, this empirically generally holds' but gives no evidence for the reasonableness of the ordering. The authors should either add a validation of the ordering (e.g., human or model-based informativeness judgments on a sample) or explicitly define D as a syntactic proxy and not claim that the partial order orders information content.
  4. [§5] The qualitative analysis is correlational, and the causal language should be softened. Section 5 reports that themes 1.5, 1.3, 1.4, 2.3, and 3.4 grow in frequency with word limit and concludes they 'might play particularly influential roles' and recommends prioritizing them in prompt design. Because the annotation is performed by an LLM (Claude Sonnet 4) using a taxonomy generated by another LLM (o3), and because theme frequency is confounded with summary length, these observations do not establish that adding those themes improves pass@1. The Limitations section acknowledges potential taxonomy bias but does not address the correlational nature of the theme-frequency analysis, and the abstract's phrase 'key drivers' overstates the evidence. A targeted ablation (adding or removing individual themes at fixed length), or at least explicit acknowledgment that these are hypotheses rather than drivers, is needed.
minor comments (4)
  1. [Abstract and §1] There are several grammar and typographical errors ('Rather then', 'thrends', 'serval limitations', 'a increase', 'generatd') that should be corrected before publication.
  2. [Figure 5 and Figure 6] The radar chart in Figure 5 and the appendix plots in Figure 6 are difficult to read; specify the number of prompts per length (284) in the caption and clarify that ptop corresponds to 'full words' in Figure 7.
  3. [§4.2] The definition of pass@1 does not state whether the hidden tests are exactly the original benchmark tests and whether partial credit is possible; please clarify the evaluation protocol.
  4. [Limitations] The Limitations section should also mention the lack of statistical uncertainty and the reference-solution confound in ptop construction, rather than only augmentation strategies, qualitative-analysis bias, and low-resource languages.

Circularity Check

2 steps flagged · score 6.0 of 10

p_top is fitted and solution-derived: it is generated from the reference solution and manually edited until Qwen2.5-Coder-14B reaches pass@1>=0.8, so the headline ParEval gains are partly by construction; the monotone trend still has independent support from intermediate levels and held-out models.

  1. fitted input called prediction [Section 3.2 (Problem Definition), Section 3.3 (Dataset Construction), and Section 4.3 (Results)]
    "We first construct a maximally detailed prompt ptop from p, constructed such that the model M generates correct solutions with high pass@1. Formally, we require E(evalp(M(ptop))) ≥ τ. In this paper we use τ = 0.8 as the threshold. ... We verify that the prompts can achieve pass@1 ≥ 0.8 for all problems with Qwen2.5-Coder-14B ... We found that 6 ParEval-Serial and 19 ParEval-OMP prompts needs to be manually edited or re-generated to make the pass@1 threshold."

    The right endpoint of every performance curve is selected on the evaluation metric itself: ptop is edited or regenerated until Qwen2.5-Coder-14B achieves pass@1 ≥ 0.8, the same model and metric for which Section 4.3 reports 0.983 on Serial and 0.967 on OMP at ptop and headlines a 0.30 absolute gain over the original OMP prompt. Since pbot for that model on OMP is 0.483, the observed gain from pbot to ptop is at least 0.317 by construction, so the magnitude of the prompt-specificity effect is not a free prediction. Intermediate summary levels were not individually threshold-selected and still rise monotonically, which supports the direction, but the headline endpoint gain is fitted rather than predicted.

  2. self definitional [Section 3.3, Appendix B, and Appendix E]
    "we generated the maximally detailed prompt ptop using the original dataset’s prompt and solution using GPT-4.1-2025-04-14 ... You should describe the function step by step, and include all the details of the function. ... Step 1: Conjugate All Complex Numbers in the Input Vector ... Step 4: Scale the Output by the Inverse of the Vector Size."

    The independent variable, prompt specificity, is operationalized with a ptop that is generated from the gold solution and explicitly instructed to describe the function step by step. The published ptop for ifft is a four-step transcription of the reference algorithm: conjugate, apply forward FFT, conjugate again, scale by 1/N. Consequently, the rising pass@1 curves largely measure how much of the reference solution can be recovered from progressively less diluted summaries or masks of ptop, rather than whether additional task specification substitutes for missing domain knowledge.

full rationale

The central quantitative claim is partially circular. The maximally detailed prompt ptop is not an independent probe of prompt specificity: it is built from the reference solution and iteratively edited until Qwen2.5-Coder-14B reaches pass@1 ≥ 0.8 on the very benchmark being analyzed, so the reported ptop scores and the headline 0.30 ParEval-OMP gain are anchored by construction. In addition, because ptop contains a step-by-step version of the gold algorithm, the experiment conflates prompt specificity with solution leakage. However, the paper does contain independent support that prevents a higher circularity score: intermediate prompts from all three augmentation families were not individually threshold-selected and still show a monotone trend; the same ptop prompts also raise pass@1 for Llama-3.x models and smaller Qwen models that were not used in the selection step; and the qualitative taxonomy analysis is a separate correlational observation. The self-citations in the related work are contextual and not load-bearing for the main result, and no uniqueness theorem or external-authority citation is used to force the framework. The detail metric D (word count, retention ratio, masking ratio) is assumed rather than validated to order information content, which is a limitation but not itself a circular step. Weighing these, the right endpoint of the effect-size claim is fitted and solution-derived, so the score is 6: partial circularity in the headline prediction, with independent evidence for the qualitative direction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical or conceptual entities are introduced with independent testable existence. PartialOrderEval is a benchmark construction, and the taxonomy is a classification scheme built by LLMs, not a posited entity.

free parameters (4)
  • ptop acceptance threshold tau = 0.8
    Section 3.2 defines tau = 0.8 as the pass@1 threshold for accepting a maximally detailed prompt; prompts that fail are manually edited or regenerated. This hand-chosen threshold sets the ceiling of every performance curve and the reported gains.
  • Summary word limits L = 10, 20, 50, 75, 100, 150, 200
    Augmentation hyperparameters defining the x-axis for LLM summarization; not fitted to data but chosen to span short to full.
  • Paragraph sampling ratios r_p = 0.2, 0.4, 0.6, 0.8
    Ratios used to randomly retain paragraphs; design choices, not fitted to outcomes.
  • Sentence block masking ratios r_s = 0.2, 0.4, 0.6, 0.8
    Masking ratios with four start positions; design choices.
assumptions (4)
  • domain assumption Greedy pass@1 (n=1) is a stable and sufficient accuracy metric for comparing prompt variants.
    Section 4.2 reports pass@1 with greedy decoding as the sole metric; no variance or repeated sampling is reported, so conclusions rest on single-run correctness.
  • domain assumption The detail metric D accurately orders prompts by information content.
    Section 3.2 defines D by construction; a 200-word summary is assumed to be more detailed than a 50-word one, but summaries may omit essential constraints at any length.
  • domain assumption Hidden test suites of HumanEval and ParEval are correct and complete proxies for program correctness.
    Pass@1 is computed against these suites; the paper does not audit them.
  • domain assumption Claude Sonnet 4 taxonomy labels are reliable, with manual verification only on a sample.
    Section 5 and the Limitations state the taxonomy was generated by o3 and labels by Claude Sonnet 4, with manual verification of a sample.

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Cite this review

Pith. "Pith review of More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation." pith.science (2026). https://pith.science/paper/JPDBA4CI

@misc{pith2026250803678,
  author       = {Pith},
  title        = {Pith review of: More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JPDBA4CI}},
  note         = {Machine review of arXiv:2508.03678}
}
read the original abstract

State-of-the-art Large Language Models (LLMs) achieve high pass@1 on general benchmarks like HumanEval but underperform on specialized suites such as ParEval. Is this due to LLMs missing domain knowledge or insufficient prompt detail is given? To answer this, we introduce PartialOrderEval, which augments any code generation benchmark with a partial order of prompts from minimal to maximally detailed. Applying it to HumanEval and both serial and OpenMP subsets of ParEval, we measure how pass@1 scales with prompt specificity. Our experiments with Llama-3.x and Qwen2.5-Coder demonstrate varying degrees of prompt sensitivity across different tasks, and a qualitative analysis highlights explicit I/O specifications, edge-case handling, and stepwise breakdowns as the key drivers of prompt detail improvement.

Figures

Figures reproduced from arXiv: 2508.03678 by the authors.

Figure 1
Figure 1. Overview of the PARTIALORDEREVAL prompt-generation pipeline. Starting from an Original Prompt and its reference Solution, we use GPT-4.1 to draft a Maximally Detailed Prompt, manually refine it to ensure a pass@1 ≥ 0.8, and designate it as ptop. From ptop, we automatically derive three families of less-detailed variants: (1) LLM Summarization at word-count limits L ∈ {10, 20, 50, 75, 100, 150, 200}, (2) Paragraph Sa… view at source ↗
Figure 2
Figure 2. The three components of a prompt: Preamble, Description and Signature, illustrated by comparing the original ParEval prompt for problem 30_scan_prefix_sum (left) with its PO-ParEval-OMP variant produced by a 25-word LLM summary (right). In PARTIALORDEREVAL, only the Description section is replaced when generating prompt variants. dataset’s prompt and solution using GPT-4.1-2025- 04-14 (OpenAI, 2025) (See Appendix B … view at source ↗
Figure 3
Figure 3. Pass@1 versus prompt detail (word limit) for LLM summarization across all [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Pass@1 versus prompt detail for Paragraph Sampling and Sentence Block Masking augmentation across [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Radar chart of the top 10 taxonomy themes [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Complete Result of select Qwen2.5-Coder and Meta-Llama3 series models on all source dataset and [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]
Figure 7
Figure 7. Figure 7: Radar Chats of all LLM Summary taxonomy themes, with one plot per category. [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 11 canonical work pages

  1. [1]

    Taxonomy Generation Prompt:Issued to o3-2025-04-16 via the ChatGPT interface, this prompt solicited a structured taxonomy based on sample LLM-summarized prompts

    work page 2025

  2. [2]

    Theme Labeling Prompt:Sent to Claude Sonnet 4 through its API, this prompt pre- sented each LLM-summarized prompt in- stance and asked the model to assign all appli- cable taxonomy themes. D.1 Taxonomy Generation Prompt # Query [all_llm_summary.txt, containing llm summaries at 7 lengths for all problems] Here is a file containing a lot of solution descrip...

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    12 # Response 1.The taxonomy should encode as much generalized information as possible

    **Intended Use**: Is the taxonomy meant for tagging/classification, search optimization, curriculum design, or something else? Clarifying these will help me tailor the taxonomy more effectively. 12 # Response 1.The taxonomy should encode as much generalized information as possible. So yes, all of these are included, but also include others if you think it...

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    Verification & Integration 10 words 25 words 50 words 75 words 100 words 150 words 200 words full words Figure 7: Radar Chats of all LLM Summary taxonomy themes, with one plot per category. 23

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    **Focus Dimensions**: Should the taxonomy categorize solutions based on the type of **algorithms**, **problem domains**, **data structures**, **implementation techniques**, or a combination of these?

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    **Desired Format**: Do you want the output in a visual hierarchy (like a tree or outline) or as a structured list?

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    output first as a detailed list of explanations, then also as a simple list, without explanation, but with numbered codes. e.g. 1. for top level and 1.1, 1.2 ... for second level codes

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    1.1 Task Goal

    it is meant for tagging/classification. D.2 Theme Labeling Prompt # System Prompt The following series of prompts are for the same problem but at different word limits. For each prompt, summarize what information is included in the prompt by assigning a code from the list below to the information that is included. Use the list of taxonomies below to guide...

    work page

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    Constraints & Robustness 10 words 25 words 50 words 75 words 100 words 150 words 200 words full words 3.1 Specifying Algorithmic Strategy 3.2 Specifying Data Structure 3.3 Forbidden T echniques 3.4 Implementation Sketch or Step Breakdown 3.5 Common Implementation Patterns 3.6 ...

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    Solution Structure & Design Guidance 10 words 25 words 50 words 75 words 100 words 150 words 200 words full words 4.1 Sample I/O Pairs 4.2 Unit T ests or Oracle Checks 4.3 Integration Context 4.4 Dependencies 0% 20% 40% 60% 80% 100%

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    Molly Q Feldman and Carolyn Jane Anderson

    Evaluating Large Language Models Trained on Code.Preprint, arXiv:2107.03374. Molly Q Feldman and Carolyn Jane Anderson. 2024. Non-Expert Programmers in the Generative AI Fu- ture. InProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work, ...

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    the function

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