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REVIEW 6 major objections 6 minor 19 references

EXECUTE: A Multilingual Benchmark for LLM Token Understanding

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

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Pith's one-line read The less an LLM knows a language, the better it performs on token-manipulation tasks, and it cannot reliably decompose Chinese, Japanese, or Korean characters into their components.

desk verdict A useful multilingual benchmark with honest measurements, but the headline inverse-resource finding is confounded with token granularity and contradicted by the paper's own Table 3. read the letter →

arxiv 2505.17784 v1 pith:LOAPEVMT submitted 2025-05-23 cs.CL

Lukas Edman , Helmut Schmid , Alexander Fraser This is my paper · ORCID
classification cs.CL
keywords LLMtokenunderstandingmultilingualbenchmarkcharactermanipulationsub-charactercomponentsCWTstatisticslow-resourcelanguagesKangxiradicalsHangulJamo
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

EXECUTE is a multilingual extension of the CUTE benchmark, built to test whether large language models can manipulate the characters and words of eight very different writing systems. The paper reports two patterns: difficulties are not always at the character level—Arabic and Russian resemble English, Hindi and Korean show a weaker version of the English pattern, Chinese and Japanese are easier at the character level than at the word level, and Amharic is nearly perfect—and performance is inversely related to how well the model knows the language. The offered explanation is that strong language knowledge biases a model toward producing real, grammatical words, so it resists the literal string manipulations the tasks demand, while weak knowledge of a low-resource language removes that pressure. The paper also introduces sub-character tasks and finds that LLMs cannot reliably decompose Chinese and Japanese characters into Kangxi radicals (the conventional component parts of Chinese characters) or Hangul syllables into their component letters (Jamo). If these claims hold, token-manipulation skill must be measured separately from language proficiency, and models lack compositional knowledge of script components.

What carries the argument

The load-bearing object is the EXECUTE benchmark itself: twelve task types (spelling, inverse spelling, contains, insertion, deletion, substitution, and swapping, each at word and character level) applied to translated TinyStories sentences in eight languages, with an expandable pipeline that requires only translation to add a language. The paper's explanation rests on two mechanisms. The first is the language's character-word-token (CWT) statistics—average characters per word, tokens per word, and characters per token—which predict the clustering of languages by task profile. The second is the model's real-word bias: when a model knows a language well, it favors existing words and grammatical forms, making it reluctant to output the ungrammatical manipulated strings the tasks require; low-resource languages, especially byte-level tokenized ones, escape this pressure. The sub-character tasks (composition and decomposition of Kangxi radicals, the conventional component parts of Chinese characters, and of Hangul Jamo, the component letters of Korean syllables) expose the absence of compositional script knowledge.

What would settle it

Translate the full prompts (not just the examples) into each of the eight target languages and re-run EXECUTE. If the inverse-resource pattern disappears—if English and Russian no longer score below Amharic, Tamazight, and Santali—then the central claim is an artifact of uneven comprehension of English prompts rather than of token-manipulation skill. For the sub-character claim, a complementary check is to test characters with unambiguous radical decompositions; near-zero scores on that subset would confirm the absence of compositional understanding, while strong scores would point to annotation ambiguity rather than missing knowledge.

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Extended reading notes

Core claim

The paper's central claim is that an LLM's ability to manipulate the characters and words of a language is governed less by how well it knows the language than by the language's writing-system geometry and the model's prior bias toward real words. Evidence comes from eight languages with very different scripts and writing systems: Russian and Arabic scores track English; Hindi and Korean follow the English direction with a smaller gap between character-level and word-level scores; Chinese and Japanese are easier at the character level because a character is usually a word; and Amharic, the lowest-resource language, approaches perfect scores on the best model. The paper interprets the Amharic result as a release from linguistic bias: when the model barely knows the language, it no longer prefers grammatical real words and can emit the literal manipulated string. The sub-character experiments extend the claim one level down: converting characters to and from their Kangxi radicals (the conventional component parts of Chinese characters) is near zero for most models, converting Hangul to and from its Jamo letters is better but still weak, and only the simpler 'does this character contain this component' task reaches moderate accuracy.

Load-bearing premise

The paper's cross-language comparisons stand or fall on the assumption that an English prompt with language-specific examples is understood consistently across all eight languages, so score differences reflect how models process characters and words rather than uneven comprehension of the task.

Editorial extensions

If this is right

  • If the inverse-resource result holds, EXECUTE scores cannot be interpreted as a measure of language proficiency: a model that knows a language better can appear worse at manipulating it.
  • If the CWT account is right, a newly added language's task profile can be predicted from its characters-per-word, tokens-per-word, and characters-per-token before any model is run.
  • A model that is told to temporarily 'forget' the target language, or that is debiased against producing grammatical output, should improve on EXECUTE in high-resource languages; the paper states this debiasing need explicitly.
  • Because the benchmark is expandable by translation alone, the same protocol can produce comparable token-understanding scores for any language, including ones not in the current eight.

Reading between the lines

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

  • Beyond the paper: if the real-word-bias mechanism is correct, then a decoding-time intervention that penalizes completions that are real words, or a prompt that asks the model to 'forget' the language, should raise EXECUTE scores for English and Russian; this is testable with the released tasks and is not claimed by the paper.
  • Beyond the paper: the CWT correlation is computed at the language level, but the same logic applies per tokenizer; recomputing tokens-per-word for each model's vocabulary and correlating within a single language would separate tokenization geometry from the model's linguistic priors.
  • Beyond the paper: the Amharic result suggests that byte-level tokenization is not inherently harmful for exact string manipulation; if so, the binding constraint in high-resource languages is the model's own language prior, and changing tokenizer granularity alone will not fix CUTE-style failures.
  • Beyond the paper: extending the sub-character tasks to other scripts with explicit components, such as Cherokee syllables or Ethiopic fidels, would show whether the missing compositional knowledge is a general property of LLMs or specific to Chinese, Japanese, and Korean.
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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

6 major / 6 minor

Summary. The paper introduces EXECUTE, a multilingual extension of the CUTE benchmark for testing LLM token understanding. It covers eight main languages spanning diverse scripts and writing systems, plus two additional low-resource languages (Tamazight, Santali) and cipher/byte-level English controls. The benchmark includes character- and word-level composition and manipulation tasks, with additional sub-character tasks for Chinese, Japanese, and Korean (radicals and Jamo). Experiments across 11 open-source LLMs yield four headline findings: non-English results often differ from English, performance correlates with character-word-token statistics, lower-resourced languages sometimes achieve higher scores, and LLMs struggle with sub-character components. The paper also provides an expandable framework and releases code.

Significance. If the central claims were fully supported, the paper would be a valuable resource for the field: it extends token-understanding evaluation to a much broader typological range than CUTE, adds sub-character probing for CJK languages, and offers an easily extensible framework with public code. The multi-model, multi-language result tables are a useful empirical contribution, and the authors explicitly release the benchmark, which should facilitate future work. The main scientific claims, however, are currently stronger than the evidence: the inverse-resource conclusion is confounded by tokenization granularity and contradicted by part of the reported data, and the claimed comparability to CUTE is not established by the appendix. With revision of these load-bearing claims, the benchmark itself remains a solid and reusable contribution.

major comments (6)
  1. [Abstract, §4.1, Table 3] The claim 'Surprisingly, the less an LLM knows a language, the better it performs on EXECUTE' is not supported by the full results. In Table 3, the best overall model (Qwen 2.5 32B) scores Hindi at 86.2, the highest of any language, while Amharic scores only 74.4; the near-perfect Amharic numbers in Table 2 come from Llama 3.3 only. The conclusion in §5 repeats the generalization, but the data show that the inverse-resource pattern is model-dependent and cannot be stated as a general property of LLMs. Please either restrict the claim to the models and languages where it holds, or provide a per-model language-knowledge proxy (e.g., perplexity or token log-likelihood) that is measured rather than assumed.
  2. [§4.1, Table 1, Table 2] The inverse-resource claim is confounded with tokenization granularity. Amharic, Tamazight, and Santali are simultaneously the lowest-resource languages and the most byte-level tokenized (c/t = 0.48, 0.49, 0.42 in Table 1), so their high scores are exactly what the paper's own CWT statistics predict. The cipher and byte-level English controls do not separate tokenization from language recognition: ciphered English changes both the tokenizer's behavior and recognizability, while byte-level English changes tokenization but retains recognition and remains poor on several word-level tasks. Moreover, Table 2 shows Cipher Inv Spell = 0.0, directly contradicting the text that 'Llama achieves near-perfect results in the low-resource languages, as well as the ciphered English.' This claim needs to be corrected, and the tokenization confound needs to be addressed explicitly, for example by holding tokenization granularity fixed across languages or by reporting results conditioned on c/t.
  3. [Appendix A, Table 5; §3.1] The assertion that 'the scores from CUTE and EXECUTE are still largely comparable' is not supported by the comparison in Table 5. For Russian, Inverse Spelling drops from 74.1 in CUTE to 37.9 in EXECUTE (a 36.2-point gap), while Swap Word rises from 33.3 to 46.5; English Insert Word also differs by about 14 points. These are not minor differences, and they undermine the claim that the changed data source, vocabulary, examples, and prompt format left results 'largely comparable.' Since the paper explicitly cites users of the English and Russian datasets to CUTE, this comparability statement is load-bearing and should be replaced by a quantified account of which tasks shift and by how much.
  4. [§3.1, §4] Cross-language comparisons assume that the English prompt texts with language-specific examples are understood equally well in all eight languages. Section 3.1 states that this design 'ensures that the LLMs understand the task consistently across languages,' but no evidence is provided for equal comprehension. If, for example, the automatically generated examples are harder or more ambiguous in some languages, every cross-language comparison, including the inverse-resource claim, would be invalidated. A concrete control would be to run a subset of models on fully translated prompts for a few languages, or to measure per-language example difficulty (e.g., by scoring the model's ability to follow the few-shot format on trivial items). Without such a check, the cross-language conclusions should be framed more cautiously.
  5. [Abstract, §4.4, Table 4] The claim that 'LLMs struggle with understanding sub-character components' is too broad. Table 4 shows that Char-to-Rad and Rad-to-Char are near zero for Chinese and Japanese, which does support a compositional-decomposition failure. However, the Contains Rad / Contains Jamo tasks are much higher (up to 96.6 for Korean), showing that models can often recognize a component when asked whether it appears in a character. The abstract and conclusion should be qualified to say that LLMs struggle with composing and decomposing characters into components, not with understanding sub-character components in general.
  6. [§4.3] The claim that 'performance is inversely correlated' with training-data size across model families is not a controlled comparison. The 7-9B models compared in that paragraph differ in tokenizer, architecture, training distribution, and training duration; training-token count alone is not a causal variable. This is a minor overinterpretation, but since it is used to bolster the inverse-resource narrative, it should either be removed or reframed as an uncontrolled observation.
minor comments (6)
  1. [Table 2] The caption says 'Llama 3.3 on low-resource languages' but the table includes English, Cipher, Byte, and Reg columns; please clarify that the table reports multiple English-variant conditions and specify what 'Reg' denotes.
  2. [Figure 2] The figure has no legend explaining the two bar colors; please add a legend or a caption note that distinguishes character-level and word-level tasks.
  3. [Table 9] The caption lists 'Hindi, Korean, Japanese, and Russian' but the table columns are ordered Hindi, Japanese, Korean, Russian; the caption order should match the table order.
  4. [§4.1, Table 2] The statement that 'Byte-level English improves character tasks but fails on word tasks' is too strong: Del Word is 96.5 and Cont Word 96.7 in the Byte column. Please specify which word tasks fail rather than saying word tasks generally fail.
  5. [Figure 4] The Japanese spelling example shows an answer identical to the input ('かわいい' → 'かわいい'), which may confuse readers because Japanese words are not written with spaces between letters; please add a note explaining how spelling is defined for logographic and syllabic scripts.
  6. [Appendix A] The comparison in Table 5 would be more informative with standard deviations or per-example counts, since several of the differences (e.g., Russian Inv Spell) are large enough to affect the paper's central conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the benchmark's claims are empirical and not reduced to their inputs.

full rationale

EXECUTE is an empirical benchmark study rather than a derivation chain. The tasks are constructed from translated TinyStories with language-specific examples, and the central results are accuracies measured on LLM outputs. The inverse-resource claim ('Surprisingly, the less an LLM knows a language, the better it performs on EXECUTE') is an interpretation of measured scores across languages chosen to vary in script and resourcedness; 'knows' is not defined in terms of EXECUTE performance, so the claim is not true by construction. The CWT statistics in Table 1 are independently computed tokenizer/character counts and are compared post hoc to performance, with no fitted parameter that forces the correlation. The main self-citation is to CUTE (Edman et al., 2024), the benchmark being extended, and to its finding about LLMs' bias toward real words. That citation is prior empirical evidence rather than an unverified premise, and Appendix A explicitly compares EXECUTE to CUTE on the same model, so the comparison does not reduce to the citation. The sub-character tasks are operational definitions of radical/Jamo manipulation, not self-referential. Weaknesses such as unmeasured per-language prompt comprehension and the tokenizer-versus-recognition confound are correctness risks, not circularity steps. No quoted step can be exhibited where a prediction is equivalent to its input by construction.

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

The benchmark has no fitted parameters in the derivational sense; the listed axioms are background assumptions in the measurement pipeline. The main free choices are design decisions (English prompts, automatic examples, Google Translate, grapheme definition), which we treat as domain assumptions rather than free parameters.

assumptions (5)
  • domain assumption English prompts with language-specific examples are understood equally well across all eight languages.
    Section 3.1 states prompts stay in English to ensure consistent task understanding, citing CUTE's Russian finding; this is assumed, not measured per language.
  • domain assumption Automatic generation of few-shot examples with the stated stipulations produces examples as effective as the manually curated CUTE examples.
    Section 3.1 describes automatic example generation; there is no validation that example difficulty is matched across languages.
  • domain assumption Kangxi radical decompositions and Hangul Jamo splits used as ground truth are canonical and unambiguous.
    Section 3.2 gives one decomposition example (晚 -> 日免) but does not describe the decomposition source or handle ambiguity, which could affect scores if multiple decompositions are valid.
  • domain assumption Google Translate outputs preserve the character and word boundaries needed for the manipulation tasks.
    Section 3.1 uses Google Translate for all non-English stories; fluent speakers verified meaning and grammar, but not task-specific boundary fidelity.
  • domain assumption CUTE and EXECUTE scores are largely comparable as claimed.
    Appendix A compares Llama 3.1 8B on both benchmarks, but large differences (e.g., Russian Inv Spell 37.9 vs 74.1) suggest the two benchmarks are not fully comparable, affecting the paper's claim that changes did not alter results.

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Cite this review

Pith. "Pith review of EXECUTE: A Multilingual Benchmark for LLM Token Understanding." pith.science (2026). https://pith.science/paper/LOAPEVMT

@misc{pith2026250517784,
  author       = {Pith},
  title        = {Pith review of: EXECUTE: A Multilingual Benchmark for LLM Token Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LOAPEVMT}},
  note         = {Machine review of arXiv:2505.17784}
}
read the original abstract

The CUTE benchmark showed that LLMs struggle with character understanding in English. We extend it to more languages with diverse scripts and writing systems, introducing EXECUTE. Our simplified framework allows easy expansion to any language. Tests across multiple LLMs reveal that challenges in other languages are not always on the character level as in English. Some languages show word-level processing issues, some show no issues at all. We also examine sub-character tasks in Chinese, Japanese, and Korean to assess LLMs' understanding of character components.

Figures

Figures reproduced from arXiv: 2505.17784 by the authors.

Figure 1
Figure 1. EXECUTE benchmark. Prompts shortened for brevity. Example of full prompt in Appendix D. Kwon et al. (2023) showed that fine-tuned models outperform prompted LLMs for Arabic. Spelling correction requires both character-level and seman￾tic knowledge to determine the correct replacement. EXECUTE, like CUTE, aims to remove contextual semantic understanding from the benchmark. Our sub-character experiments build on work … view at source ↗
Figure 2
Figure 2. The best result of all models for each language and task. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Example of full prompt for Japanese spelling, with intended output in red. [INST] and [/INST] denote any tokens added to enable normal be￾havior from each LLM. D Full Prompt Example We show an example of a full prompt in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 7, 2026 · model on record in the stance chip above.