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REVIEW 4 major objections 3 minor 3 cited by

BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models

T0 review · 4 major / 3 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper introduces BenchMAX, a 17-language, 10-task benchmark built from human-post-edited translations of existing English tests, and claims that scaling model size improves average multilingual performance but does not close the gap…

desk verdict A genuinely useful multilingual benchmark resource whose central 'fair comparison' claim is under-validated; worth reviewing with requests for more equivalence checks. read the letter →

arxiv 2502.07346 v2 pith:Y6ODMBBS submitted 2025-02-11 cs.CL

Xu Huang , Wenhao Zhu , Hanxu Hu , Conghui He , Lei Li , Shujian Huang , Fei Yuan This is my paper · ORCID
classification cs.CL
keywords BenchMAXmultilingualevaluationLLMbenchmarkinstructionfollowingcodegenerationlong-contextunderstandingcross-lingualperformancegapdomaintranslation
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

BenchMAX is a multilingual evaluation suite built to answer a practical question: whether large language models can apply their strongest skills—instruction following, reasoning, long-context understanding, code generation, tool use, and translation—in 16 non-English languages as reliably as they do in English. The paper's central claim is that the suite, whose items were machine-translated and then post-edited by three native speakers per sample, makes cross-language comparisons fair enough to reveal real capability gaps. Using it, the authors show that increasing model size raises average multilingual scores but does not systematically shrink the gap between English and other languages. The benchmark also uncovers a new task type, domain translation, where models must translate specialized content while leaving code and formatting untouched, and argues that existing translation metrics fail on it.

What carries the argument

The machinery is the translation-and-curation pipeline plus the parallel corpus it produces. Each English task is translated by machine (Google Translate when constraints are simple, GPT-4o when they are hard to extract), post-edited independently by three native-speaking annotators over at least three rounds with rule-based and model-based verifiers, and finally selected by GPT-4o-mini in position-swapped pairwise battles to remove order bias. The resulting 17-way parallel data—same items, same tasks, in every language—is what lets a score difference across languages be attributed to the model rather than to different test content. A second piece of machinery is the GAP score, the average positive English-minus-other-language difference, which is used to test whether scaling closes cross-language gaps.

What would settle it

A direct test would be to sample items from each BenchMAX task, have independent native speakers rate translation adequacy and difficulty, and compare per-item difficulty across languages using a model-blind method such as back-translation accuracy or an item-response model; if items in low-resource languages are systematically harder or more ambiguous than their English counterparts, the cross-language gap and the scaling conclusion become uninterpretable.

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Extended reading notes

Core claim

The paper claims that language-agnostic capabilities of state-of-the-art LLMs are not in fact language-agnostic when measured properly. On BenchMAX, every model family shows substantial variation across languages, with high-resource languages such as French and Chinese outperforming low-resource ones like Telugu, Swahili, and Bengali on the same items. Scaling parameter count improves average scores on nearly every task, but a family-level GAP metric that averages the English-minus-other-language score differences shows that larger models do not consistently reduce the gap; for Gemma2, the 9B model beats the 27B model on most tasks. The authors take this as evidence that cross-lingual disparity is a distinct problem from overall capability, not a residue that scale alone fixes. They also report that models from the same family correlate strongly in their per-language performance patterns, and that reasoning-distilled models improve multilingual reasoning and code generation while degrading translation.

Load-bearing premise

The load-bearing premise is that the pipeline's translated versions of every task are equivalent in difficulty and measure the same skill in all 17 languages—a premise the paper motivates with keyword-recall numbers but does not directly verify with inter-annotator agreement or an item-difficulty check.

Editorial extensions

If this is right

  • Multilingual evaluations that rely only on multiple-choice understanding tasks can miss large differences in generative skills; BenchMAX shows rankings on rule-based instruction following and code generation diverge from rankings on discriminative tasks.
  • Model scaling alone should not be expected to produce balanced multilingual ability; training data mix, alignment, and language-specific optimization are implicated instead.
  • Domain translation, where parts of the input must stay untranslated, is a distinct capability that current LLMs handle poorly and that spBLEU and TER cannot evaluate reliably.
  • Model-based instruction-following evaluation carries inherent self-bias; DeepSeek-V3 favors its own outputs when used as judge, so rankings should be read with that bias in mind.
  • BenchMAX provides a public 17-language, 10-task test bed for comparing LLMs on advanced capabilities, including low-resource languages that prior benchmarks largely omitted.

Reading between the lines

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

  • If the translation pipeline truly equalizes item difficulty across languages, BenchMAX scores can be read as a lower-bound estimate of cross-lingual transfer efficiency; a model that solves the same GPQA item in English but not Telugu is losing knowledge in transfer, not hitting a harder item.
  • The absence of reported inter-annotator agreement, validation of the GPT-4o-mini selection step, and item-difficulty invariance checks leaves open a testable extension: have a fourth human judge the three post-edited versions and compare human and LLM selections, then check per-item difficulty consistency via back-translation or statistical item-difficulty analysis.
  • The domain-translation finding suggests a practical training signal: models could be rewarded for deciding when not to translate, which naive auto-regressive translation objectives currently penalize or ignore.
  • The family-level per-language correlation result implies that scaling within a model family mostly amplifies an existing language profile rather than reshaping it, so choosing a base model for multilingual tasks may matter more than its size.
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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 / 3 minor

Summary. BenchMAX is a multilingual evaluation suite covering 17 languages and 10 tasks across six capabilities: instruction following, code generation, reasoning, long-context modeling, tool use, and translation. The non-English data are produced by machine-translating English sources into 16 languages, post-editing each sample with three native-speaking annotators, and selecting a final version with GPT-4o-mini; existing multilingual datasets such as MGSM and m-ArenaHard are extended for the remaining languages. The authors evaluate a range of open- and closed-weight LLMs and report that average performance rises with model scale while English/non-English performance gaps persist, that model families show consistent cross-language patterns, that R1-distilled models improve reasoning/code but degrade translation, and that domain-specific translation evaluation is not reliable with existing metrics. The paper claims that BenchMAX enables fair cross-language comparisons and that scaling alone cannot bridge the observed language gaps.

Significance. If the construction pipeline delivers difficulty-matched and construct-valid language versions, BenchMAX is a valuable resource: it broadens multilingual evaluation from classification-style tasks to generative capabilities, includes low-resource and non-Latin-script languages, and ships public data and code. The paper's concrete strengths are the detailed documentation of the annotation and debiasing protocol, the constraint-recall experiment (Table 5 and Table 11), the comparison against naive machine translation (Table 7), and the explicit discussion of known failure modes such as judge self-bias (Section 5.5, Figure 8) and unreliable domain-translation metrics (Section 5.3, Table 9). The headline empirical claim — that scaling does not close cross-language performance gaps — is falsifiable and of interest, but it inherits the unvalidated assumption that the 17 language versions are equivalent in difficulty and construct validity.

major comments (4)
  1. [§3.3, §5.4, Fig. 7] The central claim of fair cross-language comparison rests on translated items being equally difficult and construct-valid across languages, but the paper reports no inter-annotator agreement for the three post-editors, no human validation of the GPT-4o-mini selection step (Step 3 of §3.3), and no item-level difficulty-invariance check. The consistency analysis in §5.4 uses Llama3.1-70B and DeepSeek-V3, the same models whose scores are being compared elsewhere; high correct/incorrect agreement does not rule out a uniform difficulty shift (for example, a translated GPQA item can remain answerable but become easier because the wording is more transparent). Without such validations, the language-specific gaps in Figure 4 and the GAP conclusions in §4.2 can measure translation artifacts rather than model capability. Please add these validations or substantially weaken the 'fair comparisons' wording in the abstract and introduction.
  2. [§5.3, Tables 6 and 9] Section 5.3 demonstrates that spBLEU, TER, and XCOMET are unreliable for domain-specific translation: spBLEU and TER show extreme values on Science and Programming tasks because large portions of text remain untranslated, and XCOMET scores range from 18 to 96 with strong inconsistency across scenarios. Nevertheless, the 'Domain translation' column of Table 6 and the domain-translation correlations in Figure 6 are computed with spBLEU, the same metric the paper identifies as inflated. These numbers are therefore not directly interpretable; the translation-capability conclusions should either be restricted to general translation or reported with a metric whose validity for domain data has been established.
  3. [§5.5, Table 6, §5.6] The model-based instruction-following scores in Table 6 are win rates judged by DeepSeek-V3, and the evaluated model set includes DeepSeek-V3 itself. Section 5.5 and Figure 8 confirm that DeepSeek-V3 strongly prefers its own outputs, while GPT-4o-mini prefers GPT-4o's outputs. The paper acknowledges this self-bias but leaves the reported scores uncorrected and does not provide a sensitivity analysis under an alternative judge. Consequently, the DeepSeek-V3 comparisons in §5.6 and the GAP analysis for the model-based instruction-following task are contaminated, and the abstract's claim of fair comparisons is too strong for these numbers.
  4. [§B.5, §B.2] MGSM and m-ArenaHard are not uniformly constructed across languages: for MGSM, ten languages use the existing human-translated MGSM data while six new languages are Google-Translate-plus-check (§B.5); for m-ArenaHard, ten of the sixteen languages come from m-ArenaHard and six are newly translated (§B.2). Mixed provenance can create per-language difficulty differences that are independent of model ability. The cross-language GAP analysis should either control for provenance in the statistical comparisons or report original and newly translated languages separately, at least for the math reasoning and model-based instruction-following tasks.
minor comments (3)
  1. [§5.6, §1] The heading 'Comaparing open-source and closed-source models' contains a typo ('Comaparing' for 'Comparing'), and the first contributions bullet in Section 1 uses 'multitlingual' instead of 'multilingual'.
  2. [Table 3] The 'Translation General' row reports sample counts as '[1012, 4049]' and the 'Domain' row as 'Annotated data above 2781'; please specify whether these are per-language, per-direction, or total counts, since the interpretation of the table is otherwise ambiguous.
  3. [Appendix D, Tables 12–13] Several non-English prompt templates in Tables 12 and 13 contain visibly corrupted strings (for example, Bengali and Telugu entries such as '/bnP_RA/bnSH_NA' and 'ప"శ$'); these should be regenerated from the released code, since readers will otherwise be unable to verify the prompt templates against the public repository.

Circularity Check

1 steps flagged · score 2.0 of 10

One acknowledged self-judging loop in the model-based instruction-following task; otherwise the benchmark construction is not circular.

  1. other [Appendix A (Model-based Instruction Following); Section 5.5, Figure 8; Table 6]
    "It is assessed by the Win Rate of the testing model in comparison to the baseline model, GPT-4o, judged against DeepSeek-V3. ... DeepSeek-V3 strongly favors its own outputs, while GPT-4o-mini prefers GPT-4o's outputs."

    The model-based instruction-following score for DeepSeek-V3 in Table 6 (59.8, the highest in that column) is produced by DeepSeek-V3 acting as the judge. The paper's own Section 5.5 and Figure 8 show that DeepSeek-V3 strongly favors its own outputs, so this score is partly a measure of self-preference rather than an independent measure of instruction-following ability. The judge and the evaluated model are the same system, creating a closed evaluation loop. The paper discloses this limitation but does not correct the reported score, so the headline comparison in that column is partially determined by the judge's identity rather than by model capability.

full rationale

BenchMAX is a benchmark-construction and evaluation paper, not a mathematical derivation, so most circularity patterns do not apply. The benchmark items are produced by a transparent three-step pipeline: machine translation, three native-speaking post-editors, and GPT-4o-mini final selection. There are no fitted parameters and no prediction that reduces by construction to the paper's inputs. The self-citations (e.g., LLaMAX, Zhu et al. 2024, Yuan et al. 2020) are used for background, related work, or a symbol-extraction technique that is independently validated in Table 5; none is load-bearing for the central claims. The main cross-language gap and scaling conclusions rest on averaged scores over ten tasks, and nine of those tasks use rule-based or reference-based metrics rather than model judges. The one genuine closed loop is the model-based instruction-following task, where DeepSeek-V3 is both an evaluated model and the judge. The paper's own Section 5.5 confirms that DeepSeek-V3 strongly favors its own outputs, so its score in that single column is partly a self-preference artifact. This is acknowledged, and it does not force the central finding that cross-language gaps persist despite scaling. The absence of inter-annotator agreement statistics and item-difficulty invariance checks is a validity and correctness risk, but it is not circularity.

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

The benchmark's central claims rest on untested translation equivalence and on an LLM judge whose self-bias is acknowledged but not corrected. The listed free parameters and axioms are the load-bearing choices made during construction.

free parameters (3)
  • GEMBA-SQM quality threshold = not specified
    In Step 2 of Section 3.3, samples scoring below a predefined threshold are sent back for refinement; the threshold is never given, and scores from Qwen2.5-72B are used without calibration against human quality judgments.
  • number-word rescaling ratio = per-language ratio from Flores-200 word counts
    Applied to IFEval number-word constraints to equalize difficulty; the ratio depends on tokenizer choice and is not validated against actual model behavior.
  • annotation iteration count = 3 or more
    Each dataset undergoes at least three post-editing iterations; the stopping rule is an arbitrary quality gate, not tied to measured convergence.
assumptions (3)
  • domain assumption Translated tasks measure the same construct as English originals across languages
    Core to the 'fair comparisons' claim; no item-difficulty or invariance analysis is performed beyond keyword recall for IFEval.
  • domain assumption GPT-4o-mini reliably selects the best human translation
    Step 3 uses GPT-4o-mini pairwise battles after the human fourth annotator showed position bias; the LLM judge is never validated against human preferences, despite the paper's own finding of LLM self-bias in Section 5.5.
  • ad hoc to paper GAP aggregation is a valid measure of cross-language disparity
    GAP clips negative gaps to zero, making Qwen2.5's Korean-over-English science result invisible to the metric; the scaling conclusion in Figure 5 depends on this definition.

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Cite this review

Pith. "Pith review of BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models." pith.science (2026). https://pith.science/paper/Y6ODMBBS

@misc{pith2026250207346,
  author       = {Pith},
  title        = {Pith review of: BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y6ODMBBS}},
  note         = {Machine review of arXiv:2502.07346}
}
read the original abstract

Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, long context understanding, code generation, and so on. However, measuring these advanced capabilities across languages is underexplored. To address the disparity, we introduce BenchMAX, a multi-way multilingual evaluation benchmark that allows for fair comparisons of these important abilities across languages. To maintain high quality, three distinct native-speaking annotators independently annotate each sample within all tasks after the data was machine-translated from English into 16 other languages. Additionally, we present a novel translation challenge stemming from dataset construction. Extensive experiments on BenchMAX reveal varying effectiveness of core capabilities across languages, highlighting performance gaps that cannot be bridged by simply scaling up model size. BenchMAX serves as a comprehensive multilingual evaluation platform, providing a promising test bed to promote the development of multilingual language models. The dataset and code are publicly accessible.

Figures

Figures reproduced from arXiv: 2502.07346 by the authors.

Figure 1
Figure 1. BenchMAX evaluates diverse advanced capa [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The construction process involves three steps: Step 1) translating data from English to non-English; Step [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Flow chart illustrating the constraint extraction [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Taking two tasks as examples, models exhibit unbalanced multilingual capabilities. We show performance [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Larger models do not consistently have a [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Left: The translation performance is positively correlated with other multilingual performance. Spearman Correlations are computed between the performance on general/domain translation and the specific task. Right: Models in the same family have similar language perfor…
Figure 8
Figure 8. Figure 8: Self-bias is inevitable in model-based instruc [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: The radar charts visualize the performance of models on each subtask in different languages. Most model [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

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