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REVIEW 4 major objections 5 minor 4 references

Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Large language models lose accuracy when Korean sentences are negated, even in simple sentence-level questions, a difficulty that a new corpus-aligned benchmark measures and that generation-style fine-tuning reduces.

desk verdict A genuinely useful first Korean negation benchmark with careful construction, but the headline claim of 'significant degradation' rests on weak KMMLU evidence and an unvalidated BoolQ transformation. read the letter →

arxiv 2601.04693 v2 pith:YMCVCRRY submitted 2026-01-08 cs.CL

Sungmok Jung , Yeonkyoung So , Joonhak Lee , Sangho Kim , Yelim Ahn , Jaejin Lee This is my paper
classification cs.CL
keywords KoreannegationunderstandingLLMevaluationmultiple-choicebenchmarkstandardcorpusdistributionsupervisedfine-tuninglow-resourcelanguage
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that large language models, both Korean and non-Korean, genuinely struggle with Korean negation, and that the difficulty is measurable, systematic, and trainable. It supports this with matched comparisons where negated and affirmative versions of the same questions produce large accuracy gaps, and with a new 4,784-item multiple-choice benchmark called Thunder-KoNUBench. The benchmark asks models to select the correct standard negation of a sentence from distractors including local negation, contradiction, and paraphrase, and it is explicitly constructed to mirror the empirical distribution of negation in a large Korean corpus. Evaluating 47 models, the paper shows that larger models generally handle negation better, that instruction tuning can hurt negation performance in this low-resource language, and that fine-tuning on the benchmark improves negation understanding. The paper's core contribution is the demonstration that negation failures are not just an English phenomenon and that a corpus-aligned, sentence-level benchmark can expose and partially remedy them.

What carries the argument

The central object is 'standard negation' as defined for Korean: a recursive logical operation that negates the predicate of each main clause (or all main clauses, with De Morgan's laws for multiple main clauses), using Korean negative markers such as 안, 못, -지 않-, -지 못하-, -지 말-, or complementary antonyms. Thunder-KoNUBench operationalizes this by asking a model to select the correct standard negation from four options: standard negation, local negation, contradiction, and paraphrase. To succeed, the model must identify the main clause and its primary predicate, apply the appropriate Korean negation marker, and ignore distractors that only partially negate or merely contradict. The benchmark

What would settle it

Take the matched KMMLU and BoolQ question pairs from Section 3.3 and remove the negation from the 'negative' version without changing anything else (for example, replace 'does not refer solely' with 'does refer partly' or 'refers to more than'). If model accuracy on the rewritten affirmative versions is statistically indistinguishable from the original affirmative versions, the measured degradation is indeed caused by negation; if the gap disappears or reverses, the original comparison was confounded by surface rewording.

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Extended reading notes

Core claim

The paper demonstrates that LLMs, whether trained primarily on Korean or not, experience significant performance degradation when required to reason with negation in Korean. In matched KMMLU items, average accuracy drops from 64.6% on affirmative questions to 62.8% on negative ones; in KoBest BoolQ, negating the questions drops accuracy from 67.2% to 53.7%, with every single model for which the comparison is reported performing worse on the negated form. The paper also introduces Thunder-KoNUBench, a 4,784-item benchmark that closely follows the statistical distribution of Korean negation phenomena found in a 29,476-sentence corpus sample, where about 10.7% of sentences contain negation. On

Load-bearing premise

The matched affirmative/negated comparisons assume the two versions of each question differ only by the presence of negation, so the accuracy gap isolates negation competence rather than reflecting other changes introduced by the rewording, answer position, or plausibility of the options.

Editorial extensions

If this is right

  • Negation difficulty in Korean is not confined to complex reasoning: even simple declarative knowledge questions show measurable accuracy drops when negation is added.
  • Larger models within a model family tend to be better at Korean negation, but the improvement is not strictly monotonic, with a noticeable slowdown or temporary decline in the 8-to-12-billion-parameter range.
  • Multilingual instruction tuning can worsen negation handling in low-resource languages like Korean, especially in cloze-style evaluation, suggesting a trade-off between format fluency and linguistic robustness.
  • Fine-tuning on Thunder-KoNUBench improves Korean negation understanding and also transfers to KoBest BoolQ, a contextual reading comprehension task, without degrading performance on ARC, HellaSwag, or Winogrande.
  • Cloze-style supervision—where the model must produce the correct negated sentence—provides a stronger and more transferable training signal than symbol-style multiple-choice selection.

Reading between the lines

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

  • The cloze-versus-symbol asymmetry found here likely extends to other negation benchmarks and low-resource languages: training a model to construct a negation from scratch teaches the structural operation, while selecting among options can be gamed by surface cues. This is an editorial inference, not a claim the paper makes for other languages.
  • The benchmark's construction recipe—rule-based detection, manual verification, KL-divergence-matched distribution—could serve as a practical template for building negation benchmarks in other under-resourced languages with similar morphosyntactic complexity.
  • The non-monotonic scaling pattern suggests an intermediate-capacity regime where models have enough representational power to overfit superficial patterns but not enough linguistic supervision to master negation; a testable extension would be to add negation-focused pretraining data for models in that size range and see if the dip flattens.
  • Because the paper evaluates instruction-tuned non-Korean models in both cloze and symbol formats, the observed degradation in cloze performance hints that format-optimization during alignment may hide true negation competence in languages underrepresented in pretraining; future evaluations should include generation-style negation tasks alongside multiple-choice ones.
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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 argues that LLMs, including Korean and non-Korean models, degrade on Korean negation even in simple sentence-level tasks, based on paired affirmative/negative comparisons on KMMLU and KoBest BoolQ. It then introduces Thunder-KoNUBench, a 4,784-item multiple-choice benchmark for selecting the correct standard negation, constructed to match the empirical distribution of Korean negation markers and clause types found in a 3,160-sentence corpus sample. The paper evaluates 47 LLMs under zero-shot and few-shot settings, studies model-size and instruction-tuning trends, and reports that LoRA fine-tuning on Thunder-KoNUBench improves benchmark accuracy and, to a lesser extent, KoBest BoolQ, while leaving other tasks roughly unchanged.

Significance. If the findings hold, the paper offers a useful new resource for Korean NLP: a linguistically grounded negation benchmark with manual construction, consensus review, high human accuracy (97.6%), broad model coverage, and a public release. The corpus study, with its explicit typology of Korean negation markers and clause types, is also a helpful empirical contribution. However, the headline claim of 'significant performance degradation' rests on paired comparisons in Section 3.3 that are not validated with significance tests or transformation-quality checks, and the benchmark itself lacks an affirmative control. These issues are local and fixable; the underlying resource and evaluation effort are substantial enough to warrant a major revision rather than rejection.

major comments (4)
  1. [Section 3.3, Tables 3 and 12] The KMMLU comparison does not establish a significant degradation. The aggregate gap is only 1.8 points (62.8 vs. 64.6), and several models score higher on the negative version (e.g., EXAONE-4.0-32B 75.4 vs. 74.2; Qwen3-32B 79.5 vs. 77.7). No item counts, per-pair accuracy, confidence intervals, or paired significance tests are reported. The conversion of KMMLU questions to binary-choice items is also not described: the number of extracted items, how options were selected, and whether answer position or wording changed are unspecified. Without these details, the affirmative/negative pair may not differ only in the presence of negation.
  2. [Section 3.3, Figure 1(b), Table 12] The KoBest BoolQ transformation is the strongest empirical evidence, but its construction is not documented. The paper does not state whether the negated questions were generated manually or automatically, whether they were checked for grammaticality and semantic equivalence apart from negation, or how cases involving presupposition, double negation, or 'only' were handled. The large drop (67.2 to 53.7 overall) could be inflated by unnatural or ambiguous transformed items. Please add a detailed protocol, sample counts, and a significance test (e.g., paired test across models) to support the 'significant decline' claim.
  3. [Sections 4.2 and 5.2] Thunder-KoNUBench has no affirmative control condition; every item asks for the correct standard negation. Low zero-shot accuracy (e.g., many models below 60% in Table 13) may reflect task format, instruction complexity, or Korean-language difficulty rather than negation per se. The high human baseline shows the items are linguistically sound, but it does not isolate negation. The claim that LLMs 'encounter difficulties when handling negation' therefore depends on the Section 3.3 paired experiments, which are currently unvalidated. Please add a control condition or explicitly reinterpret the benchmark scores as measuring a composite of negation plus task-specific skills.
  4. [Section 5.2, Table 8] The statement that fine-tuning 'improves broader contextual comprehension' is overstated. The only non-negation gain of note is +3.0 on KoBest BoolQ after cloze-style SFT; KMMLU, ARC, HellaSwag, and Winogrande change by at most about 0.5 points. The large gains on Thunder-KoNUBench itself (+34.2 for cloze) are expected from matching the fine-tuning format to the evaluation task and do not demonstrate transfer. Please temper the conclusion, report significance for the BoolQ gain, and clarify what 'broader contextual comprehension' means given the other tasks are flat.
minor comments (5)
  1. [Section 3.2 / Appendix C] The corpus analysis procedure is underspecified. The rule-based negation detector is not described in enough detail to be replicated, and the manual verification step does not report annotator counts or agreement. Please provide the detection rules and inter-annotator agreement for the corpus-level classification.
  2. [Tables 2 and 5] The KL divergence values (0.007430 and 0.413696) are reported without a null distribution or threshold, so 'very small' is not well supported. Also, Table 2 uses 'Main Clause' while Table 5 uses 'Coordinated Sentence'; please reconcile the category names and clarify whether the totals are comparable.
  3. [Limitations section] The Limitations section states that lexical negation was excluded from the corpus statistics but included in Thunder-KoNUBench. This makes the 'corpus-aligned' claim partial. Please state explicitly that the distributional alignment applies only to syntactic negation markers, not to all negation phenomena in the benchmark.
  4. [Section 4.3, Table 7] The human evaluation uses only 50 questions and 10 participants; reporting only min/median/mean/max gives no measure of item-level or participant-level variance. Please provide per-participant scores or bootstrapped confidence intervals for the human baseline.
  5. [Section 7 and references] Typo: 'Acknowlegments' should be 'Acknowledgments'. Also, the AiHub reference should include a URL or dataset identifier for reproducibility. In Table 13, the API-model rows use an en dash in names; format consistently.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the empirical claims rest on external evaluations, human baselines, and held-out benchmarks, not on a self-referential derivation.

full rationale

The paper's central claims are (1) LLMs show degraded performance on Korean negation, and (2) Thunder-KoNUBench provides a valid, corpus-aligned benchmark for measuring this. Neither reduces to its own inputs. The Section 3.3 KMMLU and KoBest BoolQ experiments are direct paired manipulations of negation and are evaluated on external, pre-existing benchmarks; the affirmative/negative comparison is an empirical measurement, not a definitional tautology. The benchmark itself is validated by a human evaluation with 97.6% accuracy by non-author participants, which is independent support for the answer keys. The only self-citation of consequence is NUBench, used to supply the definitional scaffolding of standard/local negation; this is a methodological inheritance rather than a load-bearing proof of the paper's empirical findings. The corpus-distribution alignment (small KL divergence between Table 2 and Table 5) is a design/consistency check, not a fitted prediction, and it does not by itself establish any model-behavior claim. Concerns such as possible confounds in the KMMLU binary conversion, lack of confidence intervals, or unverified naturalness of BoolQ negations are validity/robustness issues, not circularity. The paper does not rename known results as new, and no fitted parameter is relabeled as a prediction. Overall, the derivation chain is self-contained and externally anchored, so the circularity score is low.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities; its central claims rest on linguistic definitions and evaluation assumptions. The benchmark's distribution is matched to the corpus by construction, not fitted.

assumptions (5)
  • domain assumption Standard and local negation definitions from NUBench are adopted as the theoretical foundation.
    The benchmark and corpus analysis rely on the NUBench distinction between standard and local negation (Section 3.1).
  • domain assumption The AiHub corpus sample is representative of general Korean text.
    The corpus statistics (Section 3.2) are based on 29,476 sampled sentences from one dataset, assumed to reflect the empirical distribution of Korean negation.
  • domain assumption The rule-based detector plus manual review correctly identifies negation instances.
    The corpus analysis and the KMMLU negation-extraction rely on this detector (Section 3.2, Appendix C).
  • domain assumption Log-likelihood-based MCQA evaluation (cloze and symbol settings) measures the intended negation competence.
    The evaluation method (Section 5.1) assumes that selecting the option with the highest log-likelihood reflects understanding of negation.
  • domain assumption The 50-item human evaluation is representative of the full 1,284-item test set.
    The reliability of the benchmark rests on this small human sample (Section 4.3).

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Cite this review

Pith. "Pith review of Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding." pith.science (2026). https://pith.science/paper/YMCVCRRY

@misc{pith2026260104693,
  author       = {Pith},
  title        = {Pith review of: Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YMCVCRRY}},
  note         = {Machine review of arXiv:2601.04693}
}
read the original abstract

Although negation is known to challenge large language models (LLMs), benchmarks for evaluating negation understanding-especially in Korean-are scarce. We conduct a corpus-based analysis of Korean negation and show that LLM performance degrades under negation. We then introduce Thunder-KoNUBench, a sentence-level negation understanding benchmark that reflects the empirical distribution of Korean negation phenomena. Evaluating 47 LLMs on Thunder-KoNUBench, we analyze the effects of model size and instruction tuning, and perform error analysis to better understand model behavior. We further show that fine-tuning on Thunder-KoNUBench improves negation understanding and broader contextual comprehension in Korean.

Figures

Figures reproduced from arXiv: 2601.04693 by the authors.

Figure 1
Figure 1. Illustration of negation-induced performance evaluation on KMMLU and KoBest BoolQ. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. An instance of Thunder-KoNUBench. 4 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Model performance across different model [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Performance change of instruction-tuned mod [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Reference graph

Works this paper leans on

4 extracted references · 3 linked inside Pith

  1. [2020]

    In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4438–4450, Online

    On negative interference in multilingual mod- els: Findings and a meta-learning treatment. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4438–4450, Online. Association for Computa- tional Linguistics. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chen...

    2020

  2. [2021]

    Edward J

    Understanding by understanding not: Model- ing negation in language models.arXiv preprint arXiv:2105.03519. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models.Preprint, arXiv:2106.09685. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzh...

    arXiv 2021

  3. [2022]

    Not another negation benchmark: The nan-nli test suite for sub-clausal negation. InProceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Lan- guage Processing (Volume 1: Long Papers), pages 883–894. Tereza Vrabcová, Marek Kadlˇcík, Petr Sojka, M...

    arXiv 2025

  4. [2025]

    알다 "(know),

    Qwen3 technical report.arXiv preprint arXiv:2505.09388. 11 Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. Hellaswag: Can a machine really finish your sentence?arXiv preprint arXiv:1905.07830. A Negative Expressions in Korean A.1 Syntactic Negation There are three main types of syntactic negation in Korean, each characterized...

    arXiv 2019

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