Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that closed-form multiple-choice questions overstate LLMs' grasp of Malaysian culture: on its 2,652-question Malay benchmark, every one of 14 tested models drops at least 17 points when the answer options are removed.

desk verdict First Malaysia-specific culture benchmark with a real empirical finding, but the guessing-theory math is wrong and the ≥17% drop is partly an artifact of free-form parsing; worth reviewing but not acceptable as is. read the letter →

arxiv 2508.05429 v2 pith:R6XRCMRB submitted 2025-08-07 cs.CL cs.AI

Zhong Ken Hew , Jia Xin Low , Sze Jue Yang , Chee Seng Chan This is my paper
classification cs.CLcs.AI
keywords MyCultureculturalbenchmarkBahasaMelayuopen-endedMCQlargelanguagemodelslow-resourcebiasMalaysian
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 sets out to show that closed-form multiple-choice benchmarks create an illusion of cultural competence in large language models, especially for a low-resource, multilingual society like Malaysia. It introduces MyCulture, 2,652 expert-validated Bahasa Melayu questions across arts, attire, customs, entertainment, food, and religion, pairing each closed-form question with an open-ended variant—multi-answer, ordering, or matching—that offers no predefined options. Across 14 models, accuracy falls by at least 17 points when the options are removed, and most models drop below 50%. The paper interprets this as evidence that LLMs do not truly grasp Malaysian culture and argues that open-ended formats should be standard in cultural evaluation. Its formal probability argument for the open-ended format contains combinatorial errors, but the empirical drop is the central evidence offered.

What carries the argument

The central mechanism is the open-ended MCQ structure with three variants—multi-answer, ordering, and matching—scored by exact-set accuracy, meaning the model's generated answer must equal the gold answer set exactly. This replaces a fixed-option guess among $m$ choices with a search over all possible subsets, sequences, or matchings, which the paper argues makes random guessing exponentially harder and makes the closed-form-to-open-ended comparison in Table 2 the main evidence for overestimated cultural competence.

What would settle it

Recompute the guessing probabilities with correct combinatorics: $\binom{k}{k}=1$ and a matching of $N$ items has $N!$ valid answers, not $\prod_{n=1}^N n^2$; then re-run MyCulture with partial-credit or human scoring on open-ended responses. If the $\geq17$-point gap shrinks or disappears, the gap is format strictness rather than missing cultural knowledge.

Watch

Extended reading notes

Core claim

The central claim is that simply shifting from closed-form to open-ended MCQs on a Malay-language cultural benchmark causes LLM performance to deteriorate by at least 17 percentage points, exposing what the paper calls an illusion of cultural knowledge. MyCulture is presented as the first culture benchmark for Malaysia, with 1,326 closed-form and 1,326 open-ended questions, all in Bahasa Melayu and validated by local experts. The open-ended variants require the model to produce the exact gold set of answers—multi-answer, an ordered sequence, or a complete matching—rather than select from options, and the paper reports that no model exceeds 38.39% on this open-ended version while several clos

Load-bearing premise

The empirical claim assumes the open-ended reformulation tests the same knowledge as the closed-form version rather than penalizing format or output compliance; the formal claim assumes the guessing-probability formulas, which are wrong as written, so the paper's stated floor on random guessing is not established.

Editorial extensions

If this is right

  • Closed-form MCQ results on cultural benchmarks should not be read as cultural competence; a model scoring above 60% on closed-form MyCulture can land below 40% on the open-ended version.
  • The open-ended format is portable: the same content can be reformulated as multi-answer, ordering, or matching questions, allowing a single dataset to measure guessing resistance across difficulty levels.
  • Regional models trained on Southeast Asian data are not automatically culturally aligned: SEA-LION 70B leads regional models but still drops 26 points, and MaLLaM models drop more than 42 points.
  • Prompt language matters more than native alignment: English and Chinese prompts outperform Malay prompts on these models, suggesting the models acquire Malaysian cultural knowledge mainly through high-resource languages.
  • Structured-output constraints change scores in both directions—DeepSeek-V3 fails to follow the schema and drops 34 points—so benchmark design must control output format.

Reading between the lines

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

  • The combinatorial errors in Section 3.4 mean the paper's theoretical guarantee is not established as written: with corrected values ($\binom{k}{k}=1$; a matching has $N!$ valid answers), open-ended formats are still harder than $1/4$ guessing for realistic sizes, but the exact factors and the claimed ordering $P_M<P_O<P_{MA}<P_{MCQ}$ would need rederivation.
  • The observed $\geq17$-point gap may partly reflect exact-set scoring strictness and output-format compliance rather than cultural knowledge alone; a partial-credit or human-scoring extension would separate format effects from knowledge effects.
  • The language-bias result suggests a testable extension: fine-tune or condition models on Malay-language cultural corpora and check whether the Malay-prompt penalty and the open-ended drop shrink together.
  • MyCulture's open-ended design could transfer to other multi-ethnic, low-resource societies, since the format does not depend on Malaysian content.
Share X Bluesky LinkedIn Reddit HN

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 introduces MyCulture, a Malay-language benchmark of 2,652 expert-validated questions on Malaysian culture across six pillars, split into closed-form and open-ended multiple-choice versions. The authors claim a theoretical justification that open-ended MCQs (multi-answer, ordering, matching) drastically reduce the probability of random guessing and therefore provide a more discriminative and fair evaluation. They evaluate 14 LLMs in zero-shot settings and report that switching from closed-form to open-ended MCQs degrades performance by at least 17 percentage points, which they interpret as exposing an 'illusion' of cultural knowledge. They also perform ablations on few-shot prompting, structured versus unstructured outputs, and prompt language.

Significance. If released and properly validated, MyCulture would be a useful resource for evaluating LLM cultural competence in a multilingual, multi-ethnic setting. The idea of comparing closed-form and open-ended versions of the same questions is also potentially valuable for understanding format bias. However, the theoretical foundation is undermined by elementary combinatorics errors, and the central empirical claim is confounded by output-format sensitivity. The dataset is not released, validation is not described, and no error bars are reported, so the main claims are not verifiable as stated.

major comments (4)
  1. [Section 3.4, Lemma 1 and Eq. (1)] Lemma 1 claims C(k,k) = k!, but C(k,k) = 1. If n = k, the only size-k subset of an n-element set is the full set, so the random-guessing probability is 1, not 1/k!. The proof conflates sets with permutations. Consequently, Lemma 2's inequality m/C(n,k) ≤ m/k! is also false in general (e.g., C(4,2)=6 < 24). The claimed factorial decay in guessability is therefore not established.
  2. [Section 4.2, Eq. (4)] The matching probability is stated as 1/∏_{n=1}^m n² = 1/(m!)². The number of bijections between two m-element sets is m!, not (m!)², so the correct probability is 1/m!. With m=4, the claimed strict ordering PM < PO < PMA < PMCQ fails: the correct values are PO=1/64≈0.0156, PM=1/24≈0.0417, PMA=1/15≈0.0667. The matching format is not the hardest under correct combinatorics.
  3. [Section 5.2 and Table 3] The '≥17% drop' is computed from unstructured open-ended outputs. Table 3 shows that switching to structured outputs changes open-ended scores by up to ±19 points (e.g., MaLLaM Tiny 2.5: 18.70→38.15; Llama-3.2 3B: 2.71→17.94). With structured outputs, the minimum drop across models is about 11 points for Llama-3.2 3B, so the 'at least 17%' and the 'illusion of cultural knowledge' conclusion are not robust to output-format handling. The authors do not describe how free-form outputs are parsed into answer sets, nor do they report multiple runs or error bars.
  4. [Section 4.1 and Section 5.1] The dataset is not released and the expert-validation procedure is not described (number of experts, qualification criteria, agreement measures). The paper says the questions are 'rigorously validated by local experts' but gives no protocol. Without dataset release and scoring details, the empirical results cannot be reproduced or independently scored.
minor comments (5)
  1. [Figure 3 and Section 4.2] The probability labels in the text are inconsistent: 'PMA(A) = 0.0156' should be PO(A); the value 0.0017 corresponds to the incorrect matching formula, not the correct 1/24 ≈ 0.0417.
  2. [Table 3 caption] The caption says 'closed-form and open-ended MCQs,' but the table compares structured and unstructured outputs within open-ended MCQs. Clarify the notation.
  3. [Notation] m is used both for the number of options and for the number of statements N; Eq. (2)–(4) would be clearer with consistent N or k.
  4. [Section 5.2] The text says 'models with over 100B parameters' for DeepSeek-V3 and Llama-4 Scout; DeepSeek-V3 has 671B total parameters but 37B active, so the description should distinguish total from active parameters.
  5. [References] Some references are incomplete (e.g., Li et al., 2024; Myung et al., 2024) and the formatting is inconsistent. Please check the bibliography.

Circularity Check

1 steps flagged · score 4.0 of 10

The theoretical 'justification' that open-ended MCQs reduce guessing is a restatement of the exact-set scoring rule; the math derivation contains errors, but the empirical evaluation is otherwise self-contained.

  1. self definitional [Section 3.2–3.4, Eq. (1)]
    "We define the exact-set accuracy as: Acc(fθ; q) = (1, if \hat S = S⋆, 0, otherwise). ... Then the probability of selecting exactly the gold answer set is: Pr(\hat S = S⋆) = 1 / C(n,k), where n = |A|."

    The paper's 'theoretical justification' that the open-ended format 'reduces guessing' and is 'stricter' is a direct consequence of the exact-set scoring rule chosen in Sec. 3.2. With exact equality required over a larger answer space, the random-guess probability is mechanically lower; the hardness is built into the metric. The paper then interprets the observed ≥17% drop as an 'illusion' of cultural knowledge without separating this constructed difficulty from actual model competence. Thus the first-principles claim reduces to the definition of the accuracy function, rather than providing independent support.

full rationale

The benchmark construction and empirical evaluation are otherwise self-contained: there is no parameter fitting called prediction, and the few self-citations (e.g., MalayMMLU for few-shot methodology) are not load-bearing. However, the paper's central theoretical claim in Sec. 3—that open-ended MCQs are harder and reduce guessing—is entailed by the exact-set scoring definition, making that part of the derivation circular by construction. The combinatorial proofs also contain errors (Lemma 1 states C(k,k)=k!, but C(k,k)=1; Eq. (4) uses ∏n² instead of N! for matchings), which weaken the independent force of the theory but are correctness issues rather than additional circularity. The empirical ≥17% drop is a real observation, but Table 3 shows large format-sensitivity (±19 points), so the 'illusion' interpretation is not uniquely forced. Overall, partial circularity in the theoretical justification, but the empirical benchmark retains independent content.

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

The benchmark has no fitted parameters. The main empirical claim depends on the quality of the expert-validated questions, the appropriateness of exact-set scoring, and the correctness of the probability calculations used to justify the open-ended format.

assumptions (3)
  • domain assumption The benchmark questions are expert-validated and culturally accurate.
    Section 4.1 states the MCQs are 'rigorously validated by local experts' but provides no details on the number of experts, their qualifications, agreement rates, or how questions were sourced or filtered.
  • domain assumption Exact-set matching (S^ = S*) is a fair measure of cultural knowledge.
    Section 3.3 defines accuracy as exact equality between the model's output set and the gold set. This penalizes models that know the correct items but fail to format them as required, conflating instruction-following with knowledge.
  • ad hoc to paper The combinatorial probabilities in Sections 3.4 and 4.2 are computed correctly.
    These derivations support the claim that open-ended MCQs reduce guessability. They contain errors (e.g., C(k,k) treated as k!, matching count as (N!)^2), so this assumption is false as stated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints." pith.science (2026). https://pith.science/paper/R6XRCMRB

@misc{pith2026250805429,
  author       = {Pith},
  title        = {Pith review of: MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R6XRCMRB}},
  note         = {Machine review of arXiv:2508.05429}
}
read the original abstract

Large Language Models (LLMs) often exhibit cultural biases due to training data dominated by high-resource languages like English and Chinese. This poses challenges for accurately representing and evaluating diverse cultural contexts, particularly in low-resource language settings. To address this, we introduce MyCulture, a benchmark designed to comprehensively evaluate LLMs on Malaysian culture across six pillars: arts, attire, customs, entertainment, food, and religion presented in Bahasa Melayu. Unlike conventional benchmarks, MyCulture employs a novel open-ended multiple-choice question format without predefined options, thereby reducing guessing and mitigating format bias. We provide a theoretical justification for the effectiveness of this open-ended structure in improving both fairness and discriminative power. Furthermore, we analyze structural bias by comparing model performance on structured versus free-form outputs, and assess language bias through multilingual prompt variations. Our evaluation across a range of regional and international LLMs reveals significant disparities in cultural comprehension, highlighting the urgent need for culturally grounded and linguistically inclusive benchmarks in the development and assessment of LLMs.

Figures

Figures reproduced from arXiv: 2508.05429 by the authors.

Figure 1
Figure 1. Visualization of the LLMs’ scores across [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Regional data distribution of MyCulture in a single format, across East and West Malaysia. The figure illustrates the number of culturally grounded ques￾tions curated for each region, highlighting differences in representation across the six cultural pillars. This distri￾bution reflects both regional diversity and the outcome of quality filtering during dataset construction. 4.1 Dataset Statistics Distribution. MyCu… view at source ↗
Figure 3
Figure 3. Visualization of the probability of correctly [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of model performance under nor [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hidden Consensus:Preference-Validity Compression in Human Feedback

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    Empirical study of Malaysian preference judgments finds that 79% of prompts have multiple majority-supported responses discarded by single-winner aggregation, indicating measurement of argmax rather than plural alignment.

Reference graph

Works this paper leans on

27 extracted references · 11 canonical work pages · cited by 1 Pith paper

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  4. [4]

    Lama Ayash, Hassan Alhuzali, Ashwag Alasmari, and Sultan Aloufi. 2025. Saudiculture: A benchmark for evaluating large language models’ cultural competence within saudi arabia. Journal of King Saud University Computer and Information Sciences, 37(6):123

    work page 2025

  5. [5]

    Debangshu Banerjee, Tarun Suresh, Shubham Ugare, Sasa Misailovic, and Gagandeep Singh. 2025. https://arxiv.org/abs/2502.09061 Crane: Reasoning with constrained llm generation . Preprint, arXiv:2502.09061

    arXiv 2025

  6. [6]

    Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, and Yejin Choi. 2025. https://arxiv.org/abs/2410.02677 Culturalbench: A robust, diverse, and challenging cultural benchmark by human-ai culturalteaming . Preprint, arXiv:2410.02677

    arXiv 2025

  7. [7]

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv--2407

    2024

  8. [8]

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. https://arxiv.org/abs/2009.03300 Measuring massive multitask language understanding . Preprint, arXiv:2009.03300

    arXiv 2021

Show all 27 references
  1. [9]

    Fajri Koto, Rahmad Mahendra, Nurul Aisyah, and Timothy Baldwin. 2024. Indoculture: Exploring geographically influenced cultural commonsense reasoning across eleven indonesian provinces. Transactions of the Association for Computational Linguistics, 12:1703--1719

    2024

  2. [10]

    Jiyoung Lee, Minwoo Kim, Seungho Kim, Junghwan Kim, Seunghyun Won, Hwaran Lee, and Edward Choi. 2024. Kornat: Llm alignment benchmark for korean social values and common knowledge. arXiv preprint arXiv:2402.13605

    2024 arXiv

  3. [11]

    Cheng Li, Mengzhuo Chen, Jindong Wang, Sunayana Sitaram, and Xing Xie. 2024. Culturellm: Incorporating cultural differences into large language models. Advances in Neural Information Processing Systems, 37:84799--84838

    2024

  4. [12]

    Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, and Timothy Baldwin. 2023. Cmmlu: Measuring massive multitask language understanding in chinese. arXiv preprint arXiv:2306.09212

    2023 arXiv

  5. [13]

    Gonzalez, and Ion Stoica

    Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, and Ion Stoica. 2024. https://lmsys.org/blog/2024-04-19-arena-hard/ From live data to high-quality benchmarks: The arena-hard pipeline

    2024

  6. [14]

    Wangyue Li, Liangzhi Li, Tong Xiang, Xiao Liu, Wei Deng, and Noa Garcia. 2024. https://arxiv.org/abs/2403.17752 Can multiple-choice questions really be useful in detecting the abilities of llms? Preprint, arXiv:2403.17752

    2024 arXiv

  7. [15]

    Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437

    2024 arXiv

  8. [16]

    Junho Myung, Nayeon Lee, Yi Zhou, Jiho Jin, Rifki Putri, Dimosthenis Antypas, Hsuvas Borkakoty, Eunsu Kim, Carla Perez-Almendros, Abinew Ali Ayele, and 1 others. 2024. Blend: A benchmark for llms on everyday knowledge in diverse cultures and languages. Advances in Neural Infor...

    2024

  9. [17]

    Raymond Ng, Thanh Ngan Nguyen, Yuli Huang, Ngee Chia Tai, Wai Yi Leong, Wei Qi Leong, Xianbin Yong, Jian Gang Ngui, Yosephine Susanto, Nicholas Cheng, and 1 others. 2025. Sea-lion: Southeast asian languages in one network. arXiv preprint arXiv:2504.05747

    2025

  10. [18]

    Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, and 1 others. 2023. Seallms--large language models for southeast asia. arXiv preprint arXiv:2312.00738

    2023 arXiv

  11. [19]

    Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, and Kiyoharu Aizawa. 2024. Jmmmu: A japanese massive multi-discipline multimodal understanding benchmark for culture-aware evaluation. arXiv preprint arXiv:2410.17250

    2024 arXiv

  12. [20]

    Soon Poh, Sze Jue Yang, Jeraelyn Tan, Lawrence Chieng, Jia Tan, Zhenyu Yu, Foong Mun, and Chee Seng Chan. 2024. Malaymmlu: A multitask benchmark for the low-resource malay language. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 650--669

    2024

  13. [21]

    Federico Raspanti, Tanir Ozcelebi, and Mike Holenderski. 2025. Grammar-constrained decoding makes large language models better logical parsers. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 485--499

    2025

  14. [22]

    W3Techs . 2025. Usage statistics of content languages for websites. https://w3techs.com/technologies/overview/content_language. Accessed: 2025-08-07

    2025

  15. [23]

    Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang, Ruyi Dai, Jen tse Huang, Zhaopeng Tu, and Michael R. Lyu. 2024 a . https://arxiv.org/abs/2310.12481 Not all countries celebrate thanksgiving: On the cultural dominance in large language models . Preprint, arXiv:2310.12481

    2024 arXiv

  16. [24]

    Xiaonan Wang, Jinyoung Yeo, Joon-Ho Lim, and Hansaem Kim. 2024 b . Kulture bench: A benchmark for assessing language model in korean cultural context. arXiv preprint arXiv:2412.07251

    2024 arXiv

  17. [25]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388

    2025 arXiv

  18. [26]

    Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023. https://arxiv.org/abs/2311.07911 Instruction-following evaluation for large language models . Preprint, arXiv:2311.07911

    2023 arXiv

  19. [27]

    Husein Zolkepli, Aisyah Razak, Kamarul Adha, and Ariff Nazhan. 2024. Mallam--malaysia large language model. arXiv preprint arXiv:2401.14680

    2024 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.