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REVIEW 4 major objections 6 minor 52 references

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper introduces ReQuesting, a prompting technique that asks an LLM to write out a plain-language algorithm of how it answered a task, and claims the algorithm is faithful when it reproduces the model's original outputs.

desk verdict A sensible prompt-based consistency measure, but the paper's leap from reproducibility to 'faithful self-explanation' is not supported by its own data. read the letter →

arxiv 2412.07781 v1 pith:WBHYCDWY submitted 2024-11-25 cs.HC cs.LG

Arion Das , Asutosh Mishra , Amitesh Patel , Soumilya De , V. Gurucharan , Kripabandhu Ghosh This is my paper · ORCID
classification cs.HCcs.LG
keywords LLMexplainabilityreproducibilitylayperson-understandablealgorithmsReQuestingfaithfulnesshigh-stakesdomainspromptengineeringzero-shotclassification
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

The paper introduces ReQuesting, a three-prompt technique that asks an LLM to (1) perform a task, (2) write out a plain-language, step-by-step algorithm that it says it used, and (3) redo the task by strictly following that algorithm. The central claim is that when the algorithm-executed outputs closely reproduce the original task outputs, the algorithm is a faithful, layperson-understandable explanation of the model's working mechanism. The authors test this on legal, financial, and mental-health classification tasks using two large language models and report high reproducibility scores, with most PerRR values above 80% and several near 98%, which they read as evidence of faithfulness. The significance is practical: if correct, non-experts could obtain a verifiable, human-readable account of what an LLM does in high-stakes decisions without access to the model's weights or internals.

What carries the argument

The central mechanism is the ReQuest prompt regime, composed of the Task Prompt, the ReQuest Prompt that extracts a natural-language algorithm A, and the Robustness Check Prompt that instructs the model to act as a bot that strictly follows steps and execute A. Faithfulness is measured by two reproducibility scores: PerRR, the percentage of the macro-F1 score that is retained when comparing task-prompt outputs to algorithm-run outputs, and PreRR, the average Jaccard overlap of the exact predicted label sets for each test instance. The operational identification of faithfulness with reproducibility is the move that carries the argument: the metrics make that identification numerically testable.

What would settle it

Run the ReQuest pipeline but replace the extracted algorithm with a placebo or deliberately corrupted one, such as a generic 'classify by your best judgment' instruction or the same steps in shuffled order, and compare the PerRR/PreRR values. If the placebo achieves reproducibility scores comparable to the real algorithm, the metric tracks prompt-recognition rather than the algorithm's content, and the faithfulness claim fails.

Watch

Extended reading notes

Core claim

The authors find that a three-prompt regime, called ReQuesting, can elicit a natural-language algorithm from an LLM and that executing that algorithm reproduces the original task output closely enough to be considered faithful. For each task, the model first answers a Task Prompt; a ReQuest Prompt then asks it to write the steps it followed and to make those steps deterministic; a Robustness Check Prompt has the same or another model execute those steps. Faithfulness is defined operationally as reproducibility: if the macro-F1 performance and the exact per-instance predictions of the algorithm-run match the original task-run closely, the algorithm is considered a faithful representation of the original mechanism. Across statute prediction, human-rights violation prediction, stock movement prediction, and two mental-health classification tasks, the paper reports PerRR (percentage macro-F1 reproduction) values mostly 82–99% for intra-LLM and inter-LLM setups, with PreRR (average Jaccard overlap of predicted label sets) generally lower, showing exact label-level reproduction is harder. They also report a small-scale manual comparison in which the structure of a generated algorithm corresponded step-by-step to reasoning elicited through top-k decoding, suggesting the algorithm is not just a surface story.

Load-bearing premise

The load-bearing premise is that comparable outputs between the original task prompt and the algorithm-executed prompt mean the algorithm reflects what the model is actually doing; without a control condition, matching outputs could simply reflect the model's tendency to give consistent answers to similarly worded prompts.

Editorial extensions

If this is right

  • If ReQuesting works, laypeople in law, medicine, and finance could ask an LLM 'what did you do?' and receive a checkable, step-by-step account of the task, which could raise trust in high-stakes AI-assisted decisions.
  • Because the extracted algorithms are executed successfully on a different LLM in the inter-LLM setups, the method suggests that task-level knowledge captured by the algorithm is portable across models, not a quirk of one model's prompting.
  • The consistently high PerRR with lower PreRR implies that generated algorithms faithfully reproduce overall performance but not the exact set of individual predictions, so case-by-case explanations would still require caution.
  • The manual alignment between the ReQuest algorithm and top-k elicited intrinsic reasoning points toward a possible bridge between behaviorally extracted explanations and mechanism-level accounts, which could become a more rigorous verification procedure.

Reading between the lines

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

  • A natural control experiment the paper does not run would test whether a placebo algorithm (generic steps, or reversed decision rules) reproduces the task outputs just as well; if it does, the reproducibility scores would measure output stability under re-prompting rather than the content of the extracted algorithm.
  • The inter-LLM reproducibility values could be inflated by the strict 'follow the steps' instruction, which is a strong output-format nudge; stripping the extracted algorithm to its semantic core or shuffling its steps would isolate what content is actually transferred.
  • Because the intrinsic-reasoning check used a much smaller model (LLaMA 3.2-1B) than the main experiments, the claimed step-wise alignment should be re-tested on the same scale before being treated as evidence of mechanism.
  • The paper's own PreRR numbers suggest that extracted algorithms are not fully deterministic at the data-point level, so a stronger validation would be checking whether the algorithm's stated conditions correspond to the model's attention or attribution patterns.
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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 / 6 minor

Summary. The paper introduces 'ReQuesting,' a prompting regime in which an LLM first performs a task (Task Prompt), then is asked to produce a layperson-understandable algorithm for the task (ReQuest Prompt), and then is asked to execute that algorithm (Robustness Check Prompt). Faithfulness of the algorithm to the LLM's internal mechanism is operationalized as reproducibility between the Task Prompt outputs and the Robustness Check outputs, measured by PerRR (ratio of Macro-F1 scores) and PreRR (Jaccard overlap of per-instance predictions). The framework is evaluated on legal statute prediction, human rights violation prediction, financial stock movement prediction, and mental-health classification tasks using gemini-1.0-pro and llama3-70b in intra- and inter-LLM setups. A third research question explores whether the generated algorithms align with the models' intrinsic reasoning by examining decoding paths on a smaller LLaMA model. The paper concludes that ReQuesting achieves faithful generation of explainable, layperson-understandable algorithms through high reproducibility.

Significance. If the central claim were established, the paper would offer a low-cost, prompt-based method for producing human-readable explanations of LLM behavior in high-stakes domains, with clear practical appeal for laypersons and domain professionals. The paper also contributes a concrete evaluation setup (Prompt/ReQuest/Robustness), two reproducibility metrics (PerRR and PreRR), and a large set of documented prompts for three domains. The manuscript's main strength is the breadth of the empirical exploration across four tasks and two proprietary/open models, and the transparency about the exact prompts used. However, the conceptual gap between 'reproducibility' and 'faithfulness to internal mechanism' is not bridged, and the paper's own data contain substantial portions (e.g., low PreRR values) that undercut the strong headline claim. The study is a useful exploratory report, but as it stands it does not provide evidence for the central claim.

major comments (4)
  1. [Section 2.1] The paper defines faithfulness as reproducibility: 'if the Robustness Check Prompt produces comparable results with the original LLM prompt (Task Prompt), then A may be considered a faithful (and explainable) representation of the complex mechanism.' This is a definitional stipulation, not an empirically established relationship. The paper then uses high reproducibility as evidence of faithful generation (e.g., in the abstract and the caption of Figure 3), making the central claim largely a restatement of the chosen metric. There is no control condition, such as executing a generic or mismatched algorithm or a scrambled version of A, to show that reproducibility is specific to the content of the generated algorithm. Without such a control, the high PerRR values could simply reflect the LLM's output consistency under similar prompts.
  2. [Tables 2-4, Appendix G] The paper's own stricter metric, PreRR, shows only moderate agreement in many configurations: e.g., Table 2 statute-prediction PreRR values are 0.5188, 0.4487, 0.6083, 0.3880, and 0.4103; Table 4 health PreRR values are 0.612, 0.701, 0.672, 0.986, 0.657, and 0.605. Yet the abstract and Figure 3 describe the results as 'high reproducibility' based on PerRR. Appendix G demonstrates a toy case where PerRR is 100% while PreRR is 0.66, explicitly showing that high PerRR does not imply prediction-level reproducibility. Thus the headline claim relies on a metric the paper itself demonstrates can be misleading, and the more faithful metric (PreRR) does not support the strong conclusion.
  3. [Section 6, Table 5] The RQ3 alignment study uses LLaMA 3.2-1B, not the gemini-1.0-pro and llama3-70b models used in the main experiments, and the paper acknowledges that 'the performance of which may not be comparable to the candidate LLMs.' The evaluation is based on manual interpretation ('In the absence of an established method, we manually verify and interpret'), with no quantitative agreement measure between the ReQuest algorithm and the intrinsic reasoning. Table 5 shows only a single anecdotal example. This evidence cannot support the claim that the ReQuest algorithms align with the intrinsic reasoning of the main models.
  4. [Limitations and Section 5.1] The Limitations section concedes that 'factors like the prompt template and context may influence reproducibility' but does not test the magnitude of this influence or compare the ReQuest regime against a baseline without any algorithm. Similarly, Section 5.1 states that 'No significant performance differences were observed across multiple runs' without reporting standard deviations, confidence intervals, or the number of runs. This lack of variance information makes it impossible to assess the stability of the reproducibility scores that are the core evidence.
minor comments (6)
  1. [Abstract] There is a typo in the abstract: 'alignent' should be 'alignment.'
  2. [Section 2.1.1] The PerRR formula is ambiguous due to missing parentheses; it is unclear whether the expression is 100 - (|diff|)/l1*100 or (100 - |diff|/l1)*100. Please clarify the mathematical notation.
  3. [Tables 2-4] The arrows (↑ and ↓) in the result tables are not explained; the caption should state what the direction of the arrow indicates (e.g., improvement or degradation relative to the Task Prompt baseline).
  4. [Table 20] There is a typo in the Robustness Check Prompt: 'Anyting else at the end' should be 'Anything else at the end.'
  5. [General] The paper does not state the test-set sizes for several of the datasets (e.g., the number of test instances for the statute prediction, stock prediction, and health tasks), which is important context when interpreting Macro-F1 and Jaccard-based scores.
  6. [Appendix G] The reference to 'Figure 5' and 'Figure 6' in the text is somewhat vague; the figures should be captioned more explicitly so the distribution plots can be tied to the claims about PerRR and PreRR behavior.

Circularity Check

1 steps flagged · score 6.0 of 10

The central faithfulness claim is self-definitional: faithfulness is stipulated as reproducibility, and high reproducibility is then reported as evidence of faithful generation.

  1. self definitional [Section 2 / 2.1 (ReQuest: Faithfulness through Reproducibility); echoed in the Abstract and Introduction.]
    "RQ2: How to determine “faithfulness”? In this course, we attempt to realize faithfulness through reproducibility. ... That is, if the Robustness Check Prompt produces comparable results with the original LLM prompt (Task Prompt), then A may be considered as a “faithful” (and explainable) representation of the complex mechanism performed through the Task Prompt. ... Table 3 shows consistently high reproducibility and hence reasonable faithfulness across the three domains."

    The target property—faithfulness of algorithm A to the LLM's internal mechanism—is stipulated as the measured quantity: comparable results between the Task Prompt and the Robustness Check Prompt. The paper then reports high values of that same measured quantity as evidence for the target property, concluding 'high reproducibility and hence reasonable faithfulness.' Thus the strongest claim, 'faithful generation ... through high degree of reproducibility,' restates the operational definition rather than deriving an independent fact. No control algorithm, external ground truth of internal computation, or independent measure is used to validate the equation between reproducibility and faithfulness.

full rationale

The circularity is partial rather than total. The underlying measurements—Macro-F1 ratios and Jaccard overlaps across intra- and inter-LLM setups—are empirical and were not fitted to force the conclusion; in principle a different algorithm could have produced low reproducibility. However, the paper's headline contribution, 'faithful generation of explainable layman-understandable algorithms,' is exactly the property it defined as reproducibility in Section 2.1. The derivation chain is therefore: define faithfulness as reproducibility, measure reproducibility, then conclude faithfulness. That is a self-definitional reduction of the central claim. The paper's RQ3 section attempts an independent check by manually comparing ReQuest algorithms with decoding-path reasoning from LLaMA 3.2-1B, but it is presented as preliminary ('we manually verify and interpret') and does not rescue the quantitative faithfulness claim. The limitations section also concedes that prompt template and context may influence reproducibility and were not controlled, which weakens the causal reading but is not itself circularity. Self-citations such as Vats et al. 2023 are used for the dataset, prompt template, and baseline, and are not load-bearing for the circularity finding. Overall, this is a clear instance of the target property being defined as the measured quantity, so the central claim reduces by construction; the score is 6 rather than higher because the measurements themselves retain independent empirical content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on equating faithfulness with reproducibility, on the reliability of LLM self-explanation, and on faithful execution of natural-language algorithms. These are domain assumptions, not demonstrated facts. No new objects or physical entities are introduced.

free parameters (3)
  • Manual prompt template refinements = not reported
    Section 5.1 describes 'extensive iterative prompt development' to make classifications exact matches; the final templates are hand-chosen and affect every reported number.
  • Top-k value for decoding-path exploration = 10
    Section 6 restricts the intrinsic reasoning exploration to k=10 without sensitivity analysis; results may depend on this choice.
  • Capitalization of prompt sections = not reported
    Section 3.3 capitalizes key prompt sections based on Sclar et al. (2024) to enforce output formats; this is a manual choice with no reported effect.
assumptions (4)
  • domain assumption Faithfulness can be operationalized as reproducibility
    Section 2.1: 'we attempt to realize faithfulness through reproducibility'; this equivalence is not independently validated.
  • domain assumption LLM self-explanations are reasonably reliable
    Section 1 cites Huang et al. (2023a) to support that ChatGPT self-explanations are on par with traditional explanation methods; this justifies relying on LLM-generated algorithms.
  • domain assumption LLMs execute natural-language algorithms as intended
    Section 2.1 relies on Zheng et al. (2024) for the executability of NL algorithms; the paper does not verify that the model actually follows the extracted algorithm rather than answering from its own priors.
  • domain assumption Comparability of minor prompt differences across models
    Sections 3.3 and 5.1 introduce LLM-specific prompt modifications for gemini and llama3, and the study treats the two models as comparable under the same task.

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Cite this review

Pith. "Pith review of Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains." pith.science (2026). https://pith.science/paper/WBHYCDWY

@misc{pith2026241207781,
  author       = {Pith},
  title        = {Pith review of: Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WBHYCDWY}},
  note         = {Machine review of arXiv:2412.07781}
}
abstract

Large Language Models (LLMs) have significantly impacted nearly every domain of human knowledge. However, the explainability of these models esp. to laypersons, which are crucial for instilling trust, have been examined through various skeptical lenses. In this paper, we introduce a novel notion of LLM explainability to laypersons, termed $\textit{ReQuesting}$, across three high-priority application domains -- law, health and finance, using multiple state-of-the-art LLMs. The proposed notion exhibits faithful generation of explainable layman-understandable algorithms on multiple tasks through high degree of reproducibility. Furthermore, we observe a notable alignment of the explainable algorithms with intrinsic reasoning of the LLMs.

Figures

Figures reproduced from arXiv: 2412.07781 by the authors.

Figure 1
Figure 1. A schematic representing the ReQuesting technique. 2 RQ1: Can the LLMs generate a faithful layperson-understandable algorithmic representation of the working mechanism? To answer this question, we propose a novel eval￾uation setup that is designed to interact with the LLMs to elicit a representative algorithm that can faithfully represent the underlying working mecha￾nism. This brings us to our next research questio… view at source ↗
Figure 2
Figure 2. A detailed schematic depicting the workflow [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. PerRR values across tasks and LLMs. The consistently high values demonstrate high reproducibility and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: An example of the novel prompt regime for the health domain. [PITH_FULL_IMAGE:figures/full_fig_p033_4.png]
Figure 5
Figure 5. Figure 5: Distribution showing no of possible unique prediction sets vs Macro-F1 for a balanced binary dataset of [PITH_FULL_IMAGE:figures/full_fig_p034_5.png]
Figure 6
Figure 6. Figure 6: Distribution showing no of possible unique prediction sets vs Macro-F1 for a balanced 3-class multi-class [PITH_FULL_IMAGE:figures/full_fig_p035_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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    Model Selection: The best performing model is selected based on the evaluation results

    Evaluation Metrics: Metrics such as accuracy, precision, recall, and F1-score are used to assess the performance of the model on a test set. Model Selection: The best performing model is selected based on the evaluation results. Step 5: Classification of New Text

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    Preprocessing: New text is processed using the same preprocessing steps as the training data

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    Feature Extraction: Features are extracted from the new text using the same techniques used for training

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    Prediction: The trained model is used to predict the class label of the new text. Example Algorithm in Python using Scikit-Learn from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.model_...

    2023

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    In Findings of the Association for Com- putational Linguistics: EMNLP 2020 , pages 2898– 2904, Online

    LEGAL-BERT: The muppets straight out of law school. In Findings of the Association for Com- putational Linguistics: EMNLP 2020 , pages 2898– 2904, Online. Association for Computational Lin- guistics. Wei Chen, Qiushi Wang, Zefei Long, Xianyin Zhang, Zhongtian Lu, Bingxuan Li, ...

    2020

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    Journal of Medical Systems, 47(1):33

    Evaluating the feasibility of chatgpt in health- care: An analysis of multiple clinical and research scenarios. Journal of Medical Systems, 47(1):33. Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Ale- tras. 2019. Neural legal judgment prediction in En- glish. In Proceedin...

    2019

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    Canadian Journal of Emergency Medicine

    Repeatability, reproducibility, and diagnos- tic accuracy of a commercial large language model (chatgpt) to perform emergency department triage using the canadian triage and acuity scale. Canadian Journal of Emergency Medicine. Daniel A. Herrmann and Benjamin A. Levinstein. 20...

    2024 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.