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REVIEW 5 major objections 6 minor 1 cited by

The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Difficulty-aware RL training can cut a reasoning model's token output by 40% without losing accuracy.

desk verdict A sensible difficulty-adaptive RL recipe that cuts tokens about 40%, but the 'improves reasoning' claim needs held-out validation because the key hyperparameter is selected on the eval benchmarks. read the letter →

arxiv 2505.19217 v1 pith:EMSI2ZU6 submitted 2025-05-25 cs.CL

Weize Chen , Jiarui Yuan , Tailin Jin , Ning Ding , Huimin Chen , Zhiyuan Liu , Maosong Sun This is my paper
classification cs.CL
keywords LLMoverthinkingtokencompressiondifficulty-awarereinforcementlearningGRPOresponselengthcontrolinferencescalingmajorityvotingreasoningefficiency
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 claims that the verbosity problem in reasoning LLMs is best attacked with a compression signal that knows how hard each problem is. It introduces DIET, a reinforcement-learning objective that estimates difficulty from the model's own rollout correctness, penalizes wordiness more on easy problems, and hands harder problems a larger token budget. In experiments on five math benchmarks, DIET raises macro-average Pass@1 from 48.6% to 50.2% while cutting average response length from 10,280 to 6,097 tokens, a roughly 40% reduction. The paper further argues that this kind of compression preserves per-sample quality, so a fixed token budget buys more majority-voting accuracy, and it strengthens the natural correlation between response length and problem difficulty. A reader should care because this is a demonstrated route to cheaper reasoning models that does not simply trade accuracy for brevity.

What carries the argument

The load-bearing object is the on-the-fly difficulty estimate $\hat{D}(x,\pi_\theta)=1-\hat{C}(x,\pi_\theta)$, where $\hat{C}$ is the fraction of $N=8$ current-policy rollouts that reach the correct answer; this is the 'difficulty' in DIET. Around it, the method builds two adaptive mechanisms, a penalty weight that grows with the model's own correctness and a per-prompt target length that grows with estimated difficulty, plus one enabling technique, Advantage Weighting, which separates the normalization of the outcome advantage from the normalization of the penalty advantage so the difficulty weight is not swallowed by outcome variance. A cosine schedule of compression pressure, $c(t)=0.5(1+\cos(2\pi t/T))$, alternates tight and relaxed compression phases during training. These pieces together let DIET push brevity where the model is already strong while preserving reasoning budget where it struggles.

What would settle it

Train DIET on a dataset that mixes easy questions whose solutions are necessarily long (proof-style simple geometry) with hard questions whose solutions are short, then compare Pass@1 against a uniform-length-penalty baseline: if DIET loses because it over-compresses the easy-but-verbose items, the monotone difficulty-length assumption is the load-bearing failure.

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Extended reading notes

Core claim

The central claim is that the optimal amount of reasoning tokens for a question is not a constant, and that a policy's own pass rate on a small sample of answers is a usable difficulty estimate. DIET folds that estimate into two simultaneous mechanisms: an adaptive penalty weight $\alpha_{ada}(x,\pi_\theta)=\alpha_{base}\cdot\hat{C}(x,\pi_\theta)$ that compresses hardest when the model already answers correctly, and a dynamic target length sampled from a difficulty-dependent interval $t(x,\pi_\theta)\sim \mathrm{Uniform}(\max(0,L_{\max}(\hat{D}-\delta)), L_{\max}\hat{D})$ so hard problems keep a larger budget. The authors also identify a failure mode of naive reward weighting inside GRPO: combining the outcome reward and the penalty before group normalization scales the penalty by $1/\sqrt{\sigma^2_{\mathrm{outcome}}+\alpha^2\sigma^2_p}$, so the intended difficulty weight is distorted by outcome variance. Their fix, Advantage Weighting, normalizes the outcome advantage and the penalty advantage separately before combining them, and the paper shows this is what makes difficulty-aware compression train stably. The empirical discovery is that the full DIET method beats every tested compression baseline in Pass@1 while reducing average tokens by about 40%, and that its shorter, difficulty-calibrated responses convert into better majority-voting accuracy at small token budgets.

Load-bearing premise

The load-bearing premise is that the correctness rate of eight sampled answers from the current policy is a stable and faithful measure of how much reasoning a problem actually needs, and that harder problems really do want longer responses across the tasks where DIET will be used.

Editorial extensions

If this is right

  • DIET-trained models keep more reasoning tokens on hard problems and compress easy ones, so the same model spends its inference budget where it matters.
  • Under a fixed total token budget, DIET reaches higher majority-voting accuracy at small budgets than prior compression methods, making sampling-based scaling practical on limited compute.
  • Difficulty-aware training can strengthen, rather than disrupt, the natural positive correlation between response length and problem difficulty.
  • The difficulty estimate reuses rollouts GRPO already samples, so the training recipe adds no extra sampling calls.

Reading between the lines

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

  • If the difficulty-length relationship holds on other domains, the same estimator could be reused at inference time to set per-prompt sampling budgets, not just training penalties.
  • Because difficulty is defined by current-policy correctness, DIET's behavior may drift when the evaluation distribution moves away from training; one testable extension is to recompute difficulty with a frozen reference policy and compare the frontier.
  • The fixed-budget voting result suggests a direct cost-per-correct-answer saving in paid APIs: shorter outputs mean more votes per dollar, although the paper does not compute such a metric.
  • The difficulty signal is orthogonal to SFT/DPO-style compression, so hybrid methods that first distill short answers and then apply DIET's adaptive pressure are a plausible untested next step.
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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 / 6 minor

Summary. The paper proposes DIET, a difficulty-aware reinforcement-learning framework for reducing reasoning-model verbosity. DIET estimates per-problem difficulty from the policy's own rollout correctness, then applies a difficulty-dependent token penalty strength and samples difficulty-conditioned length targets within GRPO. To implement these weighted objectives stably, the authors introduce 'Advantage Weighting,' which normalizes outcome and penalty terms separately before combining them, and they add a cyclical compression schedule. The main empirical claim is that on five math benchmarks with R1-Distill-Qwen-1.5B, DIET achieves the best Macro Average Pass@1 (50.2) while cutting average tokens from 10,280 to 6,097 (a 40.7% reduction), and that it improves majority-voting accuracy under fixed token budgets and strengthens the correlation between response length and estimated difficulty.

Significance. If the central claims held, DIET would be a useful contribution to a practically important problem: reducing overthinking in reasoning LLMs without sacrificing accuracy. The paper has several genuine strengths: the difficulty-length analysis in §2.3 is a clean motivating observation; the concern about naive reward weighting in group-normalized RL articulated in §3.2 and Appendix B is conceptually important; and the experimental design includes relevant baselines spanning SFT, DPO, and RL-based compression methods. The paper also makes falsifiable predictions (better inference scaling, improved length-difficulty correlation) that go beyond a single Pass@1 number. However, the evidence as presented does not yet support the strong 'simultaneously improves reasoning performance' claim, because the key trade-off parameter is selected on the same benchmarks used for evaluation, the headline results come from a single run without variance estimates, and there is an unexplained mismatch between the main-table Dynamic Target results and the ablation-table results for the same configuration.

major comments (5)
  1. [§4.2, Table 1] The headline claim that DIET achieves the best Macro Average Pass@1 rests on a single training run with no variance estimates. The reported margins over Kimi 1.5 DPO and the Base Model are only 0.3 and 1.6 points, respectively, while Pass@1 is estimated from 10 or 32 generations per problem; these differences are well within plausible sampling noise. Please report means and standard deviations across at least three independent training seeds, or provide bootstrap confidence intervals, before claiming that DIET improves reasoning performance.
  2. [Appendix F.2, Table 2] The key trade-off parameter αbase=0.5 is selected by inspecting Macro Average Pass@1 and token counts on exactly the five benchmarks that later appear in Table 1. With only three candidate values and no held-out validation, the DIET and Adaptive Weighting rows in Table 1 are in-sample selected configurations rather than unbiased evaluations of the method. Please move hyperparameter selection to a separate validation split and report the evaluation results on a held-out test set.
  3. [Appendix F.2, Table 2 vs. §4.2, Table 1] The manuscript reports two different Macro Average results for the Dynamic Target method at αbase=0.5: Table 2 gives P@1=47.4 and tokens=3349, while Table 1 gives P@1=48.6 and tokens=5657. Since Appendix F.2 states that αbase=0.5 is used for all difficulty-aware training in the main experiments, this discrepancy needs an explicit explanation (for example, a different training schedule, evaluation protocol, or checkpoint selection). As written, it is unclear which configuration actually produced the main-table results, and this undermines the reproducibility of the central comparison.
  4. [§2.2, Eq. (3); §3.1] The difficulty signal D̂(x,πθ)=1−Ĉ(x,πθ) is defined by the policy's own correctness on N=8 rollouts, and both adaptive weighting in Eq. (4) and dynamic targets in Eq. (6) are built directly from this signal. Consequently, part of the reported token-difficulty correlation and some of the performance gain may be mechanically induced by the training objective rather than reflecting an independent property of problem difficulty. Please provide a stability analysis (for example, varying N and comparing against a fixed difficulty oracle), and compare against a uniform-length compression baseline matched for total token count, to show that the difficulty-aware allocation itself is what drives the results.
  5. [Appendix B, Eq. (14)] The derivation of the 'naive reward weighting' distortion assumes that the outcome reward and the penalty term are approximately independent within a prompt batch. Under the implemented penalty fKimi in Eq. (5), the penalty sign is a function of correctness, so the two terms are structurally dependent; the claimed distortion is therefore not rigorously established for the actual objective used in the experiments. Please state the required assumptions explicitly and quantify or bound the effect of the outcome-penalty covariance, or present a derivation that does not rely on independence.
minor comments (6)
  1. [§3.1.2, Eq. (6)] The sentence immediately after Eq. (6) says the sampling procedure 'assigns shorter targets for harder problems (high D̂) and longer targets for easier ones (low D̂),' which is the opposite of what the formula and the surrounding text state. Please correct this wording.
  2. [Appendix D, heading] The heading 'Evaluation Detailes' contains a typo and should read 'Evaluation Details.'
  3. [Checklist, item 7] The checklist claims that statistical significance is reported for the experiments supporting the main claims, citing a p-value for the Pearson correlation and error bars in Fig. 2. The main claims, however, rely on Table 1, which has no error bars or significance tests; please make the checklist statement consistent with the actual evidence presented.
  4. [§4.1 and Appendix A] The empirical validation uses a single base model (R1-Distill-Qwen-1.5B), a single base algorithm (GRPO), and one training setup. Appendix A discusses domain generalization but does not mention model or algorithm generality; please add a sentence acknowledging this limitation.
  5. [Checklist, item 5] The paper states that code will be open-sourced after organization but does not release it. Given that the central results are empirical and the training details are not fully specified (for example, exact baseline hyperparameters are only referenced to original papers), a reproducibility appendix with exact commands and, ideally, a released checkpoint would substantially strengthen the submission.
  6. [Fig. 4 caption] The caption says that 'Methods that are not included have lower correlation,' but this cannot be verified from the figure alone because those methods are not plotted. Please either include all methods in the figure or state the omitted values in the text.

Circularity Check

2 steps flagged · score 6.0 of 10

The length–difficulty 'benefit' is largely a restatement of the DIET objective (Eqs. 4 and 6), and the headline Pass@1 configuration is selected on the same five benchmarks used for evaluation; the token-reduction result itself is externally supported.

  1. fitted input called prediction [Appendix F.2, Table 2; §4.2, Table 1]
    "To determine an appropriate value for the trade-off parameter αbase in Eq. (1), we conducted an ablation study on the Dynamic Target as a preliminary experiment. … We tested αbase ∈ {0.1, 0.5, 1.0}… The setting of αbase = 0.5 yields a satisfactory trade-off. Based on this balance, we selected αbase = 0.5 as the default for all the difficulty-aware training in our main experiments."

    The trade-off coefficient αbase is selected by inspecting Macro Average Pass@1 and token counts on exactly the five benchmarks (MATH 500, AIME 2024, AMC 2023, Olympiad Bench, Minerva) that later form Table 1's evaluation. The DIET row in Table 1 is therefore an in-sample selected configuration, not an independent prediction: the reported Macro Pass@1 50.2 and Tokens 6097 are the outcome of choosing αbase to look good on those same datasets. No held-out split or variance is reported, so the 'best P@1' claim over the 0.3-point margin versus Kimi DPO is statistically forced rather than independently validated.

  2. self definitional [§3.1.1 Eq. (4), §3.1.2 Eq. (6); §4.4, Fig. 4]
    "t(x, πθ) ∼ Uniform(max(0, Lmax · ( ˆD(x, πθ) − δ)), Lmax · ˆD(x, πθ)) … Fig. 4 demonstrates that our difficulty-aware methods excel at maintaining and enhancing this crucial correlation."

    Difficulty is defined by the policy's own rollouts via Eq. (3), D = 1 − Ĉ. The training objective then directly rewards conciseness when correctness is high (Eq. 4, αada increasing in Ĉ) and samples length targets t that are increasing in D (Eq. 6). Optimizing this loss pushes response length toward a target that grows with the same D used as the evaluation regressor in Fig. 4. The reported 'enhanced length–difficulty correlation' is therefore objective-satisfaction checking, not an emergent behavioral discovery: the positive Corr(D, length) is encoded in the reward by construction rather than validated against an external notion of difficulty.

full rationale

DIET's central token-efficiency comparison (10,280 → 6,097 tokens) and its inference-scaling plots are evaluated against external baselines, and the Advantage Weighting derivation in Appendix B is self-contained; there is no load-bearing self-citation or imported uniqueness theorem. The main circularity is confined to two claims. First, §4.4 presents the length–difficulty correlation as a crucial benefit, but that correlation is built into the objective: the same Eq. (3) difficulty estimate drives both the adaptive penalty (Eq. 4) and the dynamic length target (Eq. 6), so measuring Corr(D, length) with that same D is checking the model against its own training target. Second, Appendix F.2 selects αbase = 0.5 by inspecting Macro Average Pass@1 and token counts on the exact evaluation benchmarks, and Table 1 then reports those same benchmarks as the main result, making the 'best P@1' claim an in-sample selection rather than an out-of-sample prediction. Because the raw token-reduction result and the comparisons to external methods do not reduce to the objective, the paper is only partially circular.

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

The paper introduces no new physical or latent entities. Its claimed contributions rest on four hand-chosen hyperparameters governing the difficulty-aware objective and on three domain assumptions: the correctness-based difficulty proxy, the causal monotone difficulty-length relationship, and approximate independence of outcome and penalty terms. None of these are derived from first principles or independently validated, which is the main epistemic cost.

free parameters (4)
  • alpha_base = 0.5
    Overall maximum penalty strength; chosen from the ablation in Appendix F.2 using the evaluation benchmarks, then fixed for all main runs.
  • Lmax = not reported
    Maximum target length scale in Eq. (6); controls the dynamic target sampling but no value is given in Appendix C.
  • delta = 0.1 (example)
    Difficulty buffer range in the target sampling of Eq. (6); the text only gives e.g. 0.1 and no exact default.
  • cyclical compression period = half-cycle = 100
    Schedule that modulates penalty pressure via c(t); chosen by hand, no sensitivity analysis beyond Fig. 7.
assumptions (3)
  • domain assumption Difficulty can be estimated by the policy's own rollout correctness
    Eq. (3) defines D=1-C; the entire adaptive weighting depends on this proxy being meaningful and stable.
  • domain assumption Harder problems warrant longer responses
    Established as an empirical correlation in Section 2.3, then used causally in Eqs. (4) and (6) to allocate token budgets.
  • domain assumption Outcome reward and penalty are approximately independent within a prompt batch
    Appendix B's variance decomposition Var(r)=sigma^2_outcome+alpha^2 sigma^2_p relies on this; otherwise the distortion formula changes.

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Cite this review

Pith. "Pith review of The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training." pith.science (2026). https://pith.science/paper/EMSI2ZU6

@misc{pith2026250519217,
  author       = {Pith},
  title        = {Pith review of: The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EMSI2ZU6}},
  note         = {Machine review of arXiv:2505.19217}
}
read the original abstract

Recent large language models (LLMs) exhibit impressive reasoning but often over-think, generating excessively long responses that hinder efficiency. We introduce DIET ( DIfficulty-AwarE Training), a framework that systematically cuts these "token calories" by integrating on-the-fly problem difficulty into the reinforcement learning (RL) process. DIET dynamically adapts token compression strategies by modulating token penalty strength and conditioning target lengths on estimated task difficulty, to optimize the performance-efficiency trade-off. We also theoretically analyze the pitfalls of naive reward weighting in group-normalized RL algorithms like GRPO, and propose Advantage Weighting technique, which enables stable and effective implementation of these difficulty-aware objectives. Experimental results demonstrate that DIET significantly reduces token counts while simultaneously improving reasoning performance. Beyond raw token reduction, we show two crucial benefits largely overlooked by prior work: (1) DIET leads to superior inference scaling. By maintaining high per-sample quality with fewer tokens, it enables better scaling performance via majority voting with more samples under fixed computational budgets, an area where other methods falter. (2) DIET enhances the natural positive correlation between response length and problem difficulty, ensuring verbosity is appropriately allocated, unlike many existing compression methods that disrupt this relationship. Our analyses provide a principled and effective framework for developing more efficient, practical, and high-performing LLMs.

Figures

Figures reproduced from arXiv: 2505.19217 by the authors.

Figure 1
Figure 1. An overview of DIET by mitigating LLM verbosity using difficulty-aware training. A crucial dimension often overlooked in token compression is the intrinsic link between problem difficulty and the appropriate level of verbosity. We contend that a "one-size-fits-all" compression strategy is fundamentally flawed. Complex problems may necessitate longer, more detailed reasoning, whereas simpler ones should elicit direct… view at source ↗
Figure 2
Figure 2. LLM’s response length relative to prob￾lem difficulty. Before introducing our compression methods, we analyze the LLM’s natural verbosity relative to task difficulty. Using R1-Distill-Qwen-1.5B and difficulty estimated via Eq. (3), we find that solution length increases with complexity, even without difficulty-aware training [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Micro average of majority voting Pass@1 on all the benchmarks. An often-overlooked benefit of token compression is its potential to enhance inference scaling performance under a fixed total token budget. Shorter responses al￾low for more samples to be drawn for techniques like majority voting, which can improve overall accuracy if per-sample quality is maintained. While previous token compression work typically focu… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Advantage Weighting vs. Reward Weighting analysis. (Left) Training curves (Pass@1 vs. Response Length) demonstrate better performance with Advantage Weighting. (Right) Final evaluation results show Advantage Weighting yields superior performance-efficiency points. make…
Figure 4
Figure 4. Figure 4: Pearson correlation between problem difficulty and average response length of different methods. All p-values are lower than 0.01. Methods that are not included have lower correlation [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 6
Figure 6. Figure 6: Evolution of qualitative characteristics during [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Pass@1 versus average token count for Adaptive Weighting, Dynamic Target, and [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

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Reference graph

Works this paper leans on

21 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    Reduced Unnecessary Self-Doubt: The model exhibits less hesitation or redundant self- correction once a correct reasoning path is identified

    work page

  2. [2]

    Reduced Post-Solution Exploration: The model curtails exploration of alternative methods or further elaboration after a correct answer has already been found

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  3. [3]

    Improved Language Structure: The model’s output is more organized and flows logically, with fewer digressions or poorly structured sentences

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  4. [4]

    Property Satisfaction Rate

    Concise Calculation Process: Mathematical or logical steps are presented more directly and with less intermediate clutter. For this analysis, we use questions from AMC 2023, AIME 2024, and MATH 500, and compare responses from the base model against those from various checkpoints of our DIET model during its training process. To evaluate the relative impro...

    work page 2023

  5. [5]

    Guidelines: • The answer NA means that paper does not include experiments requiring code

    Open access to data and code 18 Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: We will open-source our code after proper organization. Guidelines: • The answer NA means that paper does...

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  7. [7]

    Limitations

    Limitations Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: In Appendix A, we provide an analysis of the current limitations of our work. Guidelines: • The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discusse...

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  8. [8]

    • All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced

    Theory assumptions and proofs Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [Yes] 17 Justification: We provide a derivation of advantage distortion under naive reward weighting in Appendix B Guidelines: • The answer NA means that the paper does not include theoretical ...

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  1. [9]

    We will also open-source our evaluation code after proper organization

    Experimental result reproducibility Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and da...

  2. [10]

    Guidelines: • The answer NA means that there is no societal impact of the work performed

    Broader impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [NA] Justification: Our experiments are conducted using the already widely-used open-source model, which will not induce new s...

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    Guidelines: • The answer NA means that the paper does not include experiments

    Experimental setting/details Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [Yes] Justification: We provide detailed training ...

  4. [12]

    And we reported error bars in Fig

    Experiment statistical significance Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [Yes] Justification: We conducted a p-value test when computing the Pea...

  5. [13]

    Guidelines: • The answer NA means that the paper does not include experiments

    Experiments compute resources Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [Yes] Justification: We detail the computati...

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    It does not involve human subjects, personally identifiable information, or sensitive data

    Code of ethics Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines? Answer: [Yes] Justification: Our research adheres to the NeurIPS Code of Ethics. It does not involve human s...

  7. [15]

    Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

    Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals...

  8. [16]

    Guidelines: • The answer NA means that the paper poses no such risks

    Safeguards Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: We use open-sou...

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    R1-Distilled Qwen), are properly credited

    Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: All external ass...

  10. [18]

    Guidelines: • The answer NA means that the paper does not release new assets

    New assets Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? Answer: [Yes] Justification: We will open-source our code and provide detailed documentation and com- ments for ease of use. Guidelines: • The an...

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    Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

    Crowdsourcing and research with human subjects Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? A...

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    Answer: [NA] Justification: We only used LLM for writing and editing which does not impact the core methodology

    Declaration of LLM usage Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the ...

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