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REVIEW 4 major objections 5 minor 27 references

Learning from "Silly" Questions Improves Large Language Models, But Only Slightly

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper tests whether the Ruozhiba 'silly questions' style, distilled into eight rewriting rules and applied to MMLU, improves supervised fine-tuning, and finds the overall gain is negligible even though individual tasks move by several…

desk verdict A well-motivated negative result about Ruozhiba-style augmentation, but an unverified label-preservation assumption and single-run statistics keep the headline from being clean. read the letter →

arxiv 2411.14121 v1 pith:F5QPJXQR submitted 2024-11-21 cs.CL

Tingyuan Zhu , Shudong Liu , Yidong Wang , Derek F. Wong , Han Yu , Takahiro Shinozaki , Jindong Wang This is my paper · ORCID
classification cs.CL
keywords supervisedfine-tuningdataaugmentationRuozhibaMMLUinstructionrewritingLLM-as-a-judgetask-levelconsistencyLoRA
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 asks why a Chinese website of deliberately 'silly' questions, Ruozhiba, seemed to improve LLM fine-tuning, and whether that benefit can be transferred to a standard benchmark. It uses GPT-4 to condense Ruozhiba's style into eight explanatory rules, then rewrites MMLU questions with those rules and fine-tunes Llama-3-8B-Instruct on the result. The headline result is negative overall: on the MMLU test set, the best rule-generated dataset reaches 66.28% versus 66.27% for plain seed fine-tuning. Gains and losses up to about five points appear scattered across tasks, and different rules affect the same task in the same direction more often than not. The authors suggest the original Ruozhiba advantage may have been inflated by LLM-as-judge self-preference rather than by the data itself.

What carries the argument

The central machinery is the Ruozhiba-rule augmentation pipeline: eight named rules (Counterintuitive Thinking, Blurring the Conceptual Boundaries, Anthropomorphic Expressions, and others) extracted by GPT-4 from Ruozhiba data are injected into a WizardLM-style instruction-rewriting prompt that rewrites each MMLU question while keeping reference choices and answer unchanged, and the rewritten instructions are then used as additional SFT data for LoRA fine-tuning of Llama-3-8B-Instruct. The work this machinery does is to convert an anecdotal data-source effect into a controlled, task-level evaluation of stylistic rules.

What would settle it

Take a random sample of rewritten MMLU questions from each of the eight rules, have independent human annotators (or a strong non-GPT model) answer the original multiple-choice options against the rewritten stem, and measure how often the original reference answer remains correct; if a substantial fraction are corrupted, the observed task-level improvements and drops are confounded by label noise.

Watch

Extended reading notes

Core claim

The central claim is that applying Ruozhiba-style augmentation rules to MMLU questions does not improve overall supervised fine-tuning performance beyond the seed data, and that the extracted rules act mainly as stylistic rewrites whose task-level effects are consistent regardless of which rule is used. The paper supports this with a 13,000-sample experiment: rule-generated datasets underperform plain seed fine-tuning on STEM subjects, offer small improvements on Humanities and Other subjects, and produce task-level movements such as a roughly 5% gain on Global Facts under Counterintuitive Thinking and a 6.14% drop on Econometrics under Blurring the Conceptual Boundaries. Across tasks, 94.74% showed at least 50% agreement in whether different rules helped or hurt, and 26.32% reached 100% agreement. The authors interpret this as evidence that the choice of generation rule is less critical than applying the right kind of data to the right task, and they point to LLM-as-judge self-enhancement bias as a possible reason the original Ruozhiba results were stronger.

Load-bearing premise

The whole comparison assumes that GPT-4's rewritten questions still mean the same thing and still have the same correct answer as the original MMLU question, even though the rewrite is never checked for correctness.

Editorial extensions

If this is right

  • If the central claim is right, previously reported Ruozhiba-based gains do not automatically transfer to MMLU-style multiple-choice benchmarks.
  • Task-level benefits are real but narrow, so SFT dataset construction should be guided by per-task diagnosis rather than by a one-size-fits-all augmentation style.
  • The consistency result implies that the specific rule matters less than whether augmentation fits the task, which simplifies future dataset-design decisions.
  • Filtering and mixing strategies tested in the paper do not beat the best single-rule augmentation, suggesting that selection among generated variants is not where the main gains come from.
  • The proposed explanation, LLM-as-judge self-enhancement bias, means earlier comparisons that used GPT judges may overstate the value of any particular data source.
  • The load-bearing premise is that GPT-4's rewritten questions preserve the semantics and the correct answer of the original MMLU question.

Reading between the lines

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

  • I read the near-tie on the overall MMLU average as evidence that the original Ruozhiba benefit may be a property of the evaluation setup (GPT judging GPT) rather than of 'silly' phrasing itself; a direct test would rerun the original COIG-CQIA comparison with a non-GPT judge.
  • The high consistency across rules suggests task domain is the dominant factor, which could let future work predict per-task augmentation gains from properties of the seed questions before spending API budget.
  • Because no correctness check of rewritten questions is reported, some of the observed task-level drops could be label corruption rather than a genuine stylistic effect; adding round-trip verification would cleanly separate the two.
  • A testable extension is to apply the same eight rules to open-ended tasks, where stylistic variation may matter more than it does in multiple-choice question answering.
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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 / 5 minor

Summary. The paper investigates why SFT data from the Chinese forum Ruozhiba ('silly questions') reportedly improves LLM fine-tuning. The authors use GPT-4 to extract eight stylistic/cognitive rules from Ruozhiba, then rewrite 13K MMLU training questions according to each rule while keeping the original choices and answer, fine-tune Llama-3-8B-Instruct with LoRA, and evaluate on MMLU. The main empirical finding is that no rule-generated dataset improves overall MMLU accuracy beyond the seed-only SFT baseline (best 66.28% vs 66.27%), though specific tasks show gains and drops up to about 5%. The paper also reports that the direction of task-level effects is largely consistent across rules, and that single-rule augmentation outperforms the tested mixing/filtering strategies.

Significance. If the results are valid, the paper delivers a useful, honest negative result: Ruozhiba-style rule-based rewriting does not transfer to MMLU SFT as a general gain, and its effects are concentrated in specific tasks. The work also contributes a transparent pipeline for rule extraction, eight well-documented rewriting rules, and a large set of task-level comparisons, which could be a reference point for future data augmentation studies. The significance is dampened, however, by the absence of any validation that the GPT-4 rewrites preserve the original correct answer, and by the lack of statistical support for the task-level claims; these issues are load-bearing because they determine whether the observed differences reflect the rules themselves or label noise and sampling variation.

major comments (4)
  1. [Section 3.2 / Appendix D.1]
  2. [Section 4.2 / Figures 6 and 9]
  3. [Abstract / Introduction vs Section 4.2]
  4. [Section 4.2 / Figure 14]
minor comments (5)
  1. [Section 3.2]
  2. [Abstract and Section 4.2]
  3. [References]
  4. [Figures 6 and 7]
  5. [Section 3.2]

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the MMLU result is an external benchmark comparison and is not defined by the paper's own inputs.

full rationale

The paper's central claim is an empirical comparison on the MMLU test set: fine-tuning Llama-3-8B on seed-only SFT yields 66.27% average accuracy, while the best rule-generated dataset reaches 66.28% (Section 4.2). This target quantity is not constructed from the paper's inputs: the rules are extracted from Ruozhiba via GPT-4, the augmented questions are rewritten by GPT-4, but the evaluation is performed by a different model (Meta-Llama-3-8B-Instruct) against the external MMLU test set. No parameter is fitted to the MMLU test set and then renamed a prediction; the rule sets are not optimized on MMLU. The task-level gains and drops (e.g., Counterintuitive Thinking +5% on Global Facts, Blurring the Conceptual Boundaries -6.14% on Econometrics) are reported outcomes, not derivations forced by the construction of the datasets. The only self-referential elements are minor: the same model family (GPT-4) both distills the rules and rewrites the data, and the reference list contains two background citations co-authored by one of the present authors (Shudong Liu et al. 2024a; Zhou et al. 2024), but neither is load-bearing for the central result. The paper's admitted limitation that GPT-4 rewrites are mostly stylistic and the assumption that the original response remains correct for rewritten instructions are validity risks about label preservation, not circularity: even if label noise were present, the measured MMLU accuracy would still be an external empirical observation rather than an identity with the inputs. No equation in the paper reduces a claimed prediction to a definition or to a fitted parameter, so the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No new physical or ontological entities are introduced; the eight rules are descriptive categories over existing data. The main load-bearing assumptions are about the reliability of GPT-4 as rule extractor and rewriter, and about the preservation of answer semantics after rewriting. The only hand-chosen numeric threshold is the consistency cutoff used in the summary statistics.

free parameters (1)
  • Percentage-agreement threshold = 50% (and 100% for full consistency)
    The paper's summary statistic depends on an arbitrary cutoff for what counts as consistent impact across rules; no chance baseline is provided for the consistency percentages.
assumptions (3)
  • domain assumption GPT-4's rule extraction from 240 Ruozhiba examples correctly identifies the causal properties behind Ruozhiba's reported benefit.
    Section 3.1 and Appendix C assume LLM-generated explanatory rules are faithful enough to guide augmentation; no human validation of the rules is reported.
  • domain assumption Rewriting an instruction while keeping the reference answer and choices unchanged preserves the intended semantics so the original answer remains true.
    Section 3.2 and prompt D.1 instruct GPT-4 to rewrite and keep 'Reference Answer' unchanged; correctness of the rewritten questions is not verified in the main 13K experiment.
  • domain assumption MMLU 5-shot accuracy is a valid proxy for the capabilities affected by SFT data style.
    Section 4.1 treats MMLU as representative of diverse knowledge tasks; no other benchmark is used to test generalization.

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Cite this review

Pith. "Pith review of Learning from "Silly" Questions Improves Large Language Models, But Only Slightly." pith.science (2026). https://pith.science/paper/F5QPJXQR

@misc{pith2026241114121,
  author       = {Pith},
  title        = {Pith review of: Learning from "Silly" Questions Improves Large Language Models, But Only Slightly},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F5QPJXQR}},
  note         = {Machine review of arXiv:2411.14121}
}
read the original abstract

Constructing high-quality Supervised Fine-Tuning (SFT) datasets is critical for the training of large language models (LLMs). Recent studies have shown that using data from a specific source, Ruozhiba, a Chinese website where users ask "silly" questions to better understand certain topics, can lead to better fine-tuning performance. This paper aims to explore some hidden factors: the potential interpretations of its success and a large-scale evaluation of the performance. First, we leverage GPT-4 to analyze the successful cases of Ruozhiba questions from the perspective of education, psychology, and cognitive science, deriving a set of explanatory rules. Then, we construct fine-tuning datasets by applying these rules to the MMLU training set. Surprisingly, our results indicate that rules can significantly improve model performance in certain tasks, while potentially diminishing performance on others. For example, SFT data generated following the "Counterintuitive Thinking" rule can achieve approximately a 5% improvement on the "Global Facts" task, whereas the "Blurring the Conceptual Boundaries" rule leads to a performance drop of 6.14% on the "Econometrics" task. In addition, for specific tasks, different rules tend to have a consistent impact on model performance. This suggests that the differences between the extracted rules are not as significant, and the effectiveness of the rules is relatively consistent across tasks. Our research highlights the importance of considering task diversity and rule applicability when constructing SFT datasets to achieve more comprehensive performance improvements.

Figures

Figures reproduced from arXiv: 2411.14121 by the authors.

Figure 1
Figure 1. Overview of our augmentation pipeline. that its dialogues often exhibit characteristics of humor, absurdity, linguistic traps, and abstraction that can enhance the model’s reasoning capabilities. This work attempts to present a holistic understanding of data augmentation in Ruozhiba style from two aspects: the potential interpretation of its effectiveness and a large-scale evaluation. Specifically, from the perspect… view at source ↗
Figure 2
Figure 2. The rules extracted from the Ruozhiba dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. We conduct SFT on the language model LLM using the seed dataset [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: The subject-level percentage agreement for datasets generated by the eight rules, considering whether SFT [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: We sampled the MMLU training set to include all 57 tasks, ensuring diverse task coverage for the experiment. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: The task-level accuracy changes on eight rule-generated datasets (13K samples) compared to fine-tuning with [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: The task-level accuracy on eight rule-generated datasets (13K samples) compared to fine-tuning with [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: The task-level accuracy changes on eight rule-generated datasets (4K samples) compared to fine-tuning with [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: The task-level accuracy on eight rule-generated datasets (4K samples) compared to fine-tuning with [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: The task-level accuracy changes on four filtering and mixing strategies-generated datasets (13K samples) [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: The task-level accuracy changes on four filtering and mixing strategies-generated datasets (13K samples) [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: The task-level accuracy changes on seven filtering and mixing strategies-generated datasets (4K samples) [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]
Figure 13
Figure 13. Figure 13: The task-level accuracy on seven filtering and mixing strategies-generated datasets (4K samples) compared [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]
Figure 14
Figure 14. Figure 14: Comparison of task-level percentage agreement for datasets generated by the eight rules, considering whether [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]

Discussion (0). Continue with ORCID to comment.

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