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REVIEW 4 major objections 4 minor 23 references

Investigating Numerical Translation with Large Language Models

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Open-source LLMs mistranslate large number units in Chinese–English translation about 20 percent of the time, and a post-editing step lifts accuracy to roughly 95 percent.

desk verdict A useful but sloppy empirical snapshot: large-unit numeracy errors are real, but the exact rates are unreliable because the reference lists are incomplete and unnormalized. read the letter →

arxiv 2501.04927 v1 pith:BQJSTY6M submitted 2025-01-09 cs.CL

Wei Tang , Jiawei Yu , Yuang Li , Yanqing Zhao , Weidong Zhang , Wei Feng , Min Zhang , Hao Yang This is my paper · ORCID
classification cs.CL
keywords LLMNumericalTranslationChinese-Englishlargeunitsunitconversionpost-editingpassratemachineevaluation
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 test whether large language models can be trusted to translate numbers correctly between Chinese and English, a question prior work had left largely open. It builds a bilingual dataset from real business text covering ten numerical formats—large units, ranges, decimals, fractions, ratios, ordinals, and others—and manually lists every acceptable rendering of each number so that a translation counts as correct if it matches any of them. Across nine open-source general and translation-specialized LLMs, the paper finds that numerical errors are common, with large-unit conversions such as 亿 to 'billion' the worst category: even the strongest small model, Llama3.1-8B, errs roughly 20 percent of the time. The paper then shows that a post-editing strategy—extracting the translated number pairs, converting them with deterministic tools, and replacing the wrong ones—consistently outperforms in-context learning and chain-of-thought prompting, raising large-unit pass rates to about 95 percent. The stakes are practical: in finance and medicine, a single misplaced decimal point or zero changes the meaning of the text.

What carries the argument

The load-bearing machinery is the dataset plus the pass-rate evaluation protocol. The dataset organizes 10 numerical translation types, with each test sentence carrying a manually curated reference list of every admissible rendering (for example, '2.82 billion' accepted as 2820000000, 2,820,000,000, 28.2亿, 二十八点二亿, etc.), and the Pass Rate (PR) metric counts a translation as correct only if its numerical component matches at least one reference. Against this measuring stick, the paper pits nine open-source models. The second mechanism is the post-editing (PE) framework: an LLM extracts numerical translation pairs from its own output, Python packages cn2an/en2an convert both sides to digit form, mismatched pairs are flagged, and the wrong numeral is replaced with the correctly converted digit form. PE succeeds because extraction is a task LLMs can do reliably, while the arithmetic is handed to deterministic code.

What would settle it

Have independent native speakers translate a random sample of the large-unit test sentences without seeing the reference lists, then check whether any accepted translations are absent from the paper's lists; if a substantial fraction of the sample yields valid renderings the lists omit, the reported error rates overstate how often LLMs actually get numbers wrong.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that current open-source LLMs do not reliably perform numerical translation, and that the failure is systematic rather than random: across all ten numerical types, no tested model is uniformly accurate, and the weakest type is large-unit conversion (e.g., rendering 1000亿 as 10 billion instead of 100 billion, or 134亿 as 3.4 billion instead of 13.4 billion). This weakness holds for both general-purpose LLMs and translation-specialized ones, and it is not simply a matter of model scale—the 70B version of Llama3.1 improves some categories but loses ground on ratios and formulas. The paper further claims that prompting strategies which ask the model to reason about units (in-context learning and chain-of-thought) yield only modest gains, whereas a post-editing pipeline that delegates the arithmetic to deterministic converters (cn2an and en2an) lifts large-unit pass rates to roughly 0.95 across all three weak base models tested. The authors attribute the core difficulty to LLMs' lack of a genuine unit-conversion capability and to floating-point imprecision in their internal arithmetic.

Load-bearing premise

The evaluation assumes that the manually generated reference lists in Table II contain every admissible numerical rendering for each sentence, so any missing valid translation would be counted as a model error and push the reported pass rates down.

Editorial extensions

If this is right

  • Large-unit numerical translation by open-source LLMs cannot be treated as reliable in production without a verification step, since error rates around 20 percent appear even in the newest 8B model.
  • The post-editing approach gives a model-agnostic recipe: let the LLM do the lexical work, then check every number with deterministic converters, which lifts pass rates to about 95 percent.
  • Evaluation of numerical translation should use exhaustive manual reference lists, because single-reference scoring would misclassify many valid renderings as errors.
  • Model size is not a reliable predictor of numerical accuracy; Llama3.1-70B beats the 8B on large units and fractions but falls behind on ratios and formulas.
  • The ten-type taxonomy from real business data can serve as a reusable benchmark for future numerical translation research.

Reading between the lines

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

  • The post-editing success suggests a general division of labor for calculation-heavy translation: LLMs should extract and verbalize numbers while deterministic tools perform conversions; the same recipe could extend to fractions, ranges, and formulas, which also underperform.
  • The failure pattern likely carries over to other language pairs with similar unit systems, such as Japanese 万/億 or Korean 만/억, though the paper does not test them; a cross-lingual replication would be a direct test.
  • Because the dataset is drawn from real business data, aggregate pass rates may vary by domain; finance-heavy text with more large units would show lower accuracy than text dominated by easier types like decimals.
  • The paper's error examples (e.g., 'ranked 4th' becoming 'won bronze medal') suggest that numerical mistranslation is often bound up with world-knowledge reasoning, so fixes aimed purely at arithmetic may miss a second error class.
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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 / 4 minor

Summary. This manuscript studies numerical translation between Chinese and English using a new dataset of ten numerical types. It evaluates nine open LLMs plus Google Translate, reporting pass rates per type and direction, and identifies large-unit translation (e.g., 亿, million, billion) as the hardest category. It then compares three mitigation strategies—in-context learning, chain-of-thought prompting, and an extraction-based post-editing pipeline that uses cn2an/en2an converters—and reports large gains for post-editing, with pass rates above 0.93 for large units. The paper's central claims are that numerical translation errors are common in open LLMs, that large units are the most error-prone, and that post-editing can largely fix them.

Significance. Conditional on the validity of the manual reference labels, the paper addresses an underexplored and practically important capability of LLM-based MT. Strengths include the use of real-business data, a ten-type taxonomy that goes beyond prior behavioral tests, the inclusion of several recent open LLMs, and a post-editing strategy that leans on deterministic external converters rather than fitted parameters. However, the benchmark's usefulness depends entirely on the completeness and correctness of the reference lists used for pass-rate computation, and the paper provides no confidence intervals, no decoding details, and no dataset/code release; the displayed reference lists already contain likely omissions and one numeric inconsistency. The headline error-rate claims are therefore plausible but not yet established at the reported precision.

major comments (4)
  1. [Table II / §III] Table II shows that the manually generated 'all possible translation results' are incomplete, which directly threatens the pass-rate numbers that motivate the paper. For example, the Decimal row for ZH-EN gives source '3.525' with reference '[3.525]' only, omitting the fully acceptable word-form rendering 'three point five two five'; the Large-unit row for ZH-EN gives '1.43 亿' with references that include '143 million' but omit 'one hundred forty-three million'; and the Range row for EN-ZH gives 'between 300 and 500' with references that omit the natural '在300到500之间'. Section III defines a pass as matching any reference-list item, and no normalization step is described, so exact-string matching against incomplete lists will systematically undercount valid translations. The Decimal row also lists source '1.85' with reference '[2.5, 二点五]', a numeric mismatch that indicates the manual label construction itself is error-prone. These issues must be fixed (or shown not to affect the reported error rates) before the ~20% large-unit error-rate claim can be accepted.
  2. [Table III] Table III reports pass rates without any measure of uncertainty. Sample sizes differ widely across types (e.g., Formula has 19 items in EN-ZH, Number String has 1174 in ZH-EN), so point differences of a few percentage points in small cells are not interpretable. The paper also does not state the decoding configuration (temperature, number of runs, random seeds) used to generate the translations, which matters for small open models with sampling-based decoding. Please provide confidence intervals or bootstrap estimates and specify decoding settings.
  3. [§III-B, Table V] The PE comparison in Table V is partially metric-incentivized, and the extraction step is not evaluated. PE replaces the detected numerical expression with a digit string, and the reference lists in Table II include digit strings; therefore, if the LLM extraction succeeds, the pass is achieved by construction, regardless of whether the replacement is stylistically or contextually natural. The paper should report extraction precision/recall on the large-unit test set and, ideally, a human or COMET-based acceptability check of the post-edited translations, to confirm the reported gains are not an artifact of the exact-match reference lists.
  4. [§II] The proposed dataset is the core artifact, but no public release link, annotation guidelines, or annotator-agreement statistics are provided. Since the central claims are functions of the manual labels, the dataset and the exact matching script must be made available so that the completeness of the reference lists can be audited; without this, the benchmark is not reproducible.
minor comments (4)
  1. [Introduction] In the first paragraph, the clause 'such as a decimal point, an extra zero, or a missing zero' is syntactically incomplete; it should be attached to a noun such as 'errors'.
  2. [Table III] In the ZH-EN block, after Tower-7B the label 'T-LLMs' is repeated, but the following rows (Mistral, Llama3.1, Qwen, GLM) are G-LLMs; rename the block heading.
  3. [Footnote 1] The URL points to 'Cn2An.jl', which appears to be a Julia package, while the text says 'Python packages'; verify and correct the link, since the Python package is typically 'cn2an'.
  4. [References] Reference [21] prints the author name as 'Dragos , Ciobanu' with an odd comma; fix this and make the citation formatting consistent across the reference list.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's empirical evaluation is self-contained, with the PE strategy relying on external deterministic converters and the self-citations not load-bearing.

full rationale

The paper's derivation chain is empirical: it constructs a Chinese-English numerical translation dataset with manually generated reference lists, evaluates open-source LLMs against those lists using pass rate, and compares ICL, COT, and post-editing strategies on large-unit translation. None of these steps equates an output with an input by construction. The reference labels in Table II are generated manually from real business data and are not fitted to the models being evaluated; incomplete or erroneous reference entries would be a benchmark-validity concern, not circularity. The post-editing strategy's correctness is grounded in external deterministic Python packages (cn2an and en2an) that convert number strings to digits, and the final correction is not derived from the evaluation labels or from the paper's conclusion. The self-citations that overlap with the authors ([17], and to a lesser extent [18]) are related-work citations for in-context learning and chain-of-thought prompting; they are not cited as the source of the paper's central empirical claim, so they are not load-bearing. The paper does not fit parameters, rename a known result under new coordinates, or import a uniqueness theorem from the authors' prior work. Overall, no circular step can be identified from the text.

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

The paper introduces no fitted parameters or invented entities. Its results rest on the completeness of hand-built reference labels, the representativeness of the undisclosed dataset, and the correctness of external conversion libraries.

assumptions (3)
  • domain assumption The manually generated reference lists include every acceptable numerical translation.
    Pass rate is computed as a match against these lists (Section III, evaluation metric). If a valid translation is missing, error rates are overstated.
  • domain assumption The test sentences are representative of real business data.
    The dataset is sampled from real business data, but the sampling procedure and source are not described (Section II).
  • domain assumption The Python packages cn2an and en2an correctly convert all numbers in the dataset.
    The PE strategy relies on these external converters for detection and correction; no failure cases are reported (Section III-B).

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Cite this review

Pith. "Pith review of Investigating Numerical Translation with Large Language Models." pith.science (2026). https://pith.science/paper/BQJSTY6M

@misc{pith2026250104927,
  author       = {Pith},
  title        = {Pith review of: Investigating Numerical Translation with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BQJSTY6M}},
  note         = {Machine review of arXiv:2501.04927}
}
read the original abstract

The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made significant advancements in machine translation, their capacity for translating numbers has not been thoroughly explored. This study focuses on evaluating the reliability of LLM-based machine translation systems when handling numerical data. In order to systematically test the numerical translation capabilities of currently open source LLMs, we have constructed a numerical translation dataset between Chinese and English based on real business data, encompassing ten types of numerical translation. Experiments on the dataset indicate that errors in numerical translation are a common issue, with most open-source LLMs faltering when faced with our test scenarios. Especially when it comes to numerical types involving large units like ``million", ``billion", and "yi", even the latest llama3.1 8b model can have error rates as high as 20%. Finally, we introduce three potential strategies to mitigate the numerical mistranslations for large units.

Figures

Figures reproduced from arXiv: 2501.04927 by the authors.

Figure 1
Figure 1. The framework of PE strategy. Llama3.1-8B as the base model and its framework can be seen in the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

23 extracted references · 20 canonical work pages

  1. [17]

    Submodular-based in-context example selection for LLMs-based machine translation,

    Baijun Ji, Xiangyu Duan, Zhenyu Qiu, Tong Zhang, Junhui Li, Hao Yang, and Min Zhang, “Submodular-based in-context example selection for LLMs-based machine translation,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024, pp. 15398–15409

    work page 2024

  2. [1]

    Bleu: a method for automatic evaluation of machine translation,

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA. 2002, pp. 311– 318, ACL

    work page 2002

  3. [2]

    COMET: A neural framework for MT evaluation,

    Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie, “COMET: A neural framework for MT evaluation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, Eds. Nov. 2020, Association for Computational Linguistics

    work page 2020

  4. [3]

    As easy as 1, 2, 3: Behavioural testing of NMT systems for numerical translation,

    Jun Wang, Chang Xu, Francisco Guzm ´an, Ahmed El-Kishky, Benjamin I. P. Rubinstein, and Trevor Cohn, “As easy as 1, 2, 3: Behavioural testing of NMT systems for numerical translation,” in Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021, 2021, vol. ACL/IJCNLP 2021 of Findings of ACL, pp. 4711–4717

    work page 2021

  5. [4]

    Ar- tifacts or abduction: How do llms answer multiple-choice questions without the question?,

    Nishant Balepur, Abhilasha Ravichander, and Rachel Rudinger, “Ar- tifacts or abduction: How do llms answer multiple-choice questions without the question?,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024. 2024, pp. 10308– 10330, Association...

    work page 2024

  6. [5]

    Exploring precision and recall to assess the quality and diversity of llms,

    Florian Le Bronnec, Alexandre Verine, Benjamin N ´egrevergne, Yann Chevaleyre, and Alexandre Allauzen, “Exploring precision and recall to assess the quality and diversity of llms,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2024. 2024, pp. ...

    work page 2024

  7. [6]

    Boosting LLMS with ontology-aware prompt for ner data augmentation,

    Zhizhao Luo, Youchen Wang, Wenjun Ke, Rui Qi, Yikai Guo, and Peng Wang, “Boosting LLMS with ontology-aware prompt for ner data augmentation,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, Seoul, Republic of Korea, April 14-19, 2024. 2024, pp. 12361–12365, IEEE

    work page 2024

  8. [7]

    Eliciting the translation ability of large language models via multilingual finetuning with translation instructions,

    Jiahuan Li, Hao Zhou, Shujian Huang, Shanbo Cheng, and Jiajun Chen, “Eliciting the translation ability of large language models via multilingual finetuning with translation instructions,” Transactions of the Association for Computational Linguistics, vol. 12, pp. 576–592, 2024

    work page 2024

Show all 23 references
  1. [8]

    Multilingual machine translation with large language models: Empirical results and analysis,

    Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li, “Multilingual machine translation with large language models: Empirical results and analysis,” in Findings of the Association for Computational Linguistics: NAACL 2024, Me...

    2024

  2. [9]

    The llama 3 herd of models,

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, et al., “The llama 3 herd of models,” 2024

    2024

  3. [10]

    Tower: An open multilingual large language model for translation-related tasks,

    Duarte M. Alves, Jos ´e Pombal, Nuno M. Guerreiro, Pedro H. Martins, Jo˜ao Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, Jos ´e G. C. de Souza, and Andr ´e F. T. Martins, “Tower: An open multilingual large language model for t...

    2024

  4. [11]

    Qwen2 technical report,

    An Yang, Baosong Yang, Binyuan Hui, et al., “Qwen2 technical report,” arXiv preprint arXiv:2407.10671, 2024

    2024 arXiv

  5. [12]

    Mistral 7b,

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L ´elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thoma...

    2023

  6. [13]

    Crosslingual generalization through multitask finetuning,

    Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, et al., “Crosslingual generalization through multitask finetuning,” arXiv preprint arXiv:2211.01786, 2022

    2022 arXiv

  7. [14]

    Scaling neural machine translation to 200 languages,

    NLLB Team, “Scaling neural machine translation to 200 languages,” Nat., vol. 630, no. 8018, pp. 841–846, 2024

    2024

  8. [15]

    In-context examples selection for ma- chine translation,

    Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad, “In-context examples selection for ma- chine translation,” in Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023. 2023, pp. 8857–8873, Assoc...

    2023

  9. [16]

    Towards robust in- context learning for machine translation with large language mod- els,

    Shaolin Zhu, Menglong Cui, and Deyi Xiong, “Towards robust in- context learning for machine translation with large language mod- els,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), ...

    2024

  10. [18]

    Towards making the most of ChatGPT for machine translation,

    Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao, “Towards making the most of ChatGPT for machine translation,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, Dec. 2023, pp. 5622–5633

    2023

  11. [19]

    xcot: Cross-lingual instruction tuning for cross-lingual chain-of- thought reasoning,

    Linzheng Chai, Jian Yang, Tao Sun, Hongcheng Guo, Jiaheng Liu, Bing Wang, Xinnian Liang, Jiaqi Bai, Tongliang Li, Qiyao Peng, and Zhoujun Li, “xcot: Cross-lingual instruction tuning for cross-lingual chain-of- thought reasoning,” CoRR, vol. abs/2401.07037, 2024

    2024 arXiv

  12. [20]

    M 3cot: A novel benchmark for multi-domain multi-step multi- modal chain-of-thought,

    Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, and Wanxiang Che, “M 3cot: A novel benchmark for multi-domain multi-step multi- modal chain-of-thought,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (V olume1: Long Papers), AC...

    2024

  13. [21]

    Error annotation in post-editing machine translation: Investigating the impact of text-to-speech technology,

    Justus Brockmann, Claudia Wiesinger, and Dragos , Ciobanu, “Error annotation in post-editing machine translation: Investigating the impact of text-to-speech technology,” in Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, Ghent, Be...

    2022

  14. [22]

    Guiding large language models to post-edit machine translation with error annotations,

    Dayeon Ki and Marine Carpuat, “Guiding large language models to post-edit machine translation with error annotations,” in Findings of the Association for Computational Linguistics: NAACL 2024, Mexico City, Mexico, June 16-21, 2024, Kevin Duh, Helena G ´omez-Adorno, and Steven ...

    2024

  15. [23]

    Contextual refinement of translations: Large language models for sentence and document-level post-editing,

    Sai Koneru, Miriam Exel, Matthias Huck, and Jan Niehues, “Contextual refinement of translations: Large language models for sentence and document-level post-editing,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Lingui...

    2024

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Reviewed August 10, 2026 · model on record in the stance chip above.