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REVIEW 5 major objections 5 minor 15 references

Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: A Pilot Study

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Traditional machine-translation services generally outperform large language models on automated fidelity metrics for medical consultation summaries in Arabic, Chinese, and Vietnamese, especially for complex clinician-facing text, a pilot…

desk verdict Honest, tiny pilot: traditional MT edges out LLMs on surface metrics, but n=2 docs and possibly MT-seeded references keep the headline provisional. read the letter →

arxiv 2504.16601 v1 pith:Z5NKN5GA submitted 2025-04-23 cs.CL cs.AI

Andy Li , Wei Zhou , Rashina Hoda , Chris Bain , Peter Poon This is my paper · ORCID
classification cs.CLcs.AI
keywords machinetranslationlargelanguagemodelsmedicalconsultationsummaryresponsibleAIBLEUCHR-FMETEOR
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This pilot study compares three general-purpose large language models—GPT-4o, LLAMA-3.1, and GEMMA-2—with three established machine-translation services—Google Translate, Microsoft Bing Translator, and DeepL—on translating two simulated medical consultation summaries from English into Arabic, simplified Chinese, and Vietnamese: a simple, patient-facing summary and a complex, clinician-oriented letter. Its central finding is that the traditional MT services generally score higher on surface-fidelity metrics—word-level and character-level overlap with professional references—especially for the complex summary, while some LLMs match or beat them on METEOR, a metric that aligns on meaning and tolerates paraphrasing, for Vietnamese and Chinese on the simpler patient text. Arabic runs the opposite way: scores rise with text complexity, which the authors attribute to richer morphological context aiding disambiguation. The paper argues that automated metrics do not capture clinical adequacy and that human oversight remains necessary. If correct, the study indicates that freely available general-purpose LLMs are not yet safe substitutes for domain-vetted or human-reviewed translation in healthcare.

What carries the argument

The central machinery is the reference-anchored metric triad: BLEU counts word-level n-gram overlap, CHR-F counts character-level n-gram overlap, and METEOR aligns words with synonym and stemming tolerance to approximate semantic similarity. All three are computed against professional third-party reference translations of two deliberately contrasted English summaries—a simple lay-language patient summary and a complex jargon-dense clinician letter—across three languages. The simple-versus-complex contrast is the controlled variable that exposes language-specific behavior, such as Arabic benefiting from additional context and Chinese degrading sharply on technical content.

What would settle it

Translate a larger corpus of consultation summaries—say 50 simple and 50 complex per language—with the same six systems, score them against independently produced human references verified to contain no machine-translation influence, and check whether LLMs match or beat Google Translate, Bing, and DeepL on BLEU, CHR-F, and METEOR for complex texts; if they do, the paper's claim of a general traditional-MT advantage is overturned.

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Extended reading notes

Core claim

The paper asserts that when medical consultation summaries are translated from English into Arabic, simplified Chinese, and Vietnamese, traditional MT tools—Google Translate, Microsoft Bing Translator, and DeepL—generally outperform the tested LLMs—GPT-4o, LLAMA-3.1, and GEMMA-2—on surface-level metrics (BLEU and CHR-F), with the advantage most pronounced for the complex clinician-oriented summary. Against that pattern, LLAMA-3.1 and GEMMA-2 achieve METEOR scores in Vietnamese and Chinese comparable to or above the traditional tools on the simpler patient-facing summary, and Arabic translation quality improves with complexity across most systems because longer, context-rich sentences help resolve the language's morphological ambiguities. Chinese shows the steepest drop from simple to complex text, which the authors tie to syntactic and terminological challenges. The paper presents these results as an indicative pilot comparison, not a definitive quality ranking, and stresses that the metrics cannot measure clinical safety.

Load-bearing premise

The load-bearing premise is that the professional reference translations are an unbiased ground truth, even though the paper concedes translators may have used machine-generated drafts as a starting point—if those references were influenced by MT output, the scores are biased toward systems resembling those drafts.

Editorial extensions

If this is right

  • Healthcare workers using a free default tool on complex clinician-oriented text would currently get closer surface-level fidelity from Google Translate or Bing than from the tested general-purpose LLMs.
  • For patient-facing Vietnamese and Chinese, an LLM such as LLAMA-3.1 or GEMMA-2 can produce translations whose semantic similarity to a professional reference rivals or exceeds traditional MT, making them plausible draft tools for simpler text.
  • Automatic metric scores cannot certify a medical translation as safe: a mistranslated drug name and a missing filler word can receive the same penalty, so clinical review remains necessary before use.
  • Arabic-language medical translation appears to improve with longer, more context-rich input, a pattern that runs opposite to the Chinese and Vietnamese results.

Reading between the lines

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

  • Editorial inference: the paper acknowledges that the professional reference translators may have used machine-generated drafts, so the metric scores could systematically favor outputs resembling those drafts; scoring against independently produced human references would test whether the traditional-MT advantage persists.
  • Editorial inference: because each language is represented by only one simple and one complex summary, the language-level patterns are provisional; a larger corpus of consultation summaries would show whether Vietnamese resilience and the Arabic complexity gain are stable.
  • Editorial inference: a clinician-oriented error-severity evaluation—counting mistranslated medication names, dosages, and instructions—would likely rank the systems differently from BLEU, CHR-F, and METEOR and give a more actionable safety signal for deployment.
  • Editorial inference: the Vietnamese METEOR results suggest fine-tuning LLMs on biomedical Vietnamese corpora is a plausible next step, but the paper does not test whether such fine-tuning would close the surface-fidelity gap with traditional MT.
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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

5 major / 5 minor

Summary. This pilot study compares three large language models (GPT-4o, GEMMA-2, LLAMA-3.1) with three traditional machine translation tools (Google Translate, Microsoft Bing Translator, DeepL) for translating two English medical consultation summaries into Arabic, Chinese, and Vietnamese. One summary is a simple, patient-facing document and the other is a complex, clinician-oriented letter with medical jargon. Translation quality is measured with BLEU, chrF, and METEOR against professional third-party reference translations. The authors report that traditional MT tools generally scored higher on surface-level metrics, especially for the complex summaries, while some LLMs achieved comparable or higher METEOR scores in Vietnamese and Chinese. They also report language-specific trends, such as Chinese showing a larger score drop from simple to complex summaries and Arabic showing relative improvement on the complex summary. The paper concludes that current automatic metrics are insufficient for clinical translation quality, that LLMs remain inconsistent, and that human oversight is necessary.

Significance. If the empirical pattern holds, the study offers a timely, domain-specific caution that general-purpose LLMs may currently lag behind established MT services on lexical fidelity for medical documents in low- and medium-resource languages, while also indicating that LLMs can be competitive on metrics that tolerate paraphrase. The strengths of the paper are its transparent design, the use of two expert-authored fictitious consultation summaries to avoid privacy issues, the engagement of a certified translation service for reference translations, and the candid acknowledgment of limitations in both the reference construction and the automatic metrics. However, because each condition is a single output scored against a single presumably independent reference, and because the reference may have been influenced by machine translation drafts, the quantitative conclusions should be treated as preliminary and requiring stronger validation before informing clinical practice.

major comments (5)
  1. [Methods, Reference Translations] The paper explicitly admits that 'some degree of influence from machine-generated drafts might still persist in the reference translations' (Methods, Reference Translations). This is a load-bearing confound for the abstract's central claim that traditional MT tools generally outperformed LLMs on surface-level metrics: BLEU and chrF reward lexical and character overlap, so a reference that was seeded or post-edited from Google Translate, Bing, or DeepL will inflate the scores of systems whose outputs resemble those drafts and penalize fluent LLM paraphrases. The manuscript does not state which MT engines, if any, the translators used, nor how much post-editing occurred, so the direction and magnitude of the bias cannot be assessed. The authors should either obtain an MT-free reference translation or a second reference, or explicitly reframe every metric-based conclusion as relative to the specific professional references used, rather than as a general statement about translation quality.
  2. [Methods, Statistical Analysis; Table 1] The evaluation consists of 34 translations with exactly one output per condition and one reference translation, and the paper reports no variance estimates, confidence intervals, or significance tests. For example, in the Vietnamese simple summary Google Translate's BLEU of 0.7719 is compared with LLAMA's 0.7517, a difference of 0.02 on a single document; without bootstrap over sentences, multiple independent references, or repeated generation runs, such differences cannot be distinguished from noise. The conclusion that traditional MT tools 'generally outperformed' LLMs is therefore not statistically supported. The authors should add uncertainty quantification, or explicitly restrict all conclusions to descriptive comparisons of the particular outputs shown in Figure 1.
  3. [Results, Simple vs. Complex Summary Translation Performance] The comparison between the simple and complex summaries is confounded with document identity: the two English source texts differ not only in complexity but also in content, length, and clinical scenario. Therefore statements such as 'Arabic translations improved with complexity due to the language's morphology' and 'Chinese showed the most performance decline with increased complexity' are not supported as causal claims about complexity. To support such conclusions, the authors would need multiple paired documents varying complexity while holding content constant, or a substantially larger sample of source documents. As it stands, these are descriptive differences between two specific texts, and the causal language in the Abstract and Results should be softened accordingly.
  4. [Methods, Statistical Analysis; Table 2] Table 2 and the Statistical Analysis section describe METEOR as measuring 'similarity at the semantic level' and offering 'a balance between surface and semantic matching.' In fact, METEOR aligns surface word forms with optional exact, stem, and synonym matching; it does not compute meaning-based semantic similarity. Consequently, the Results claim that LLAMA and GEMMA achieve 'competitive or superior METEOR scores' in Vietnamese and Chinese should not be interpreted as evidence of superior semantic preservation. The metric description and any language about semantic accuracy in the Discussion need to be revised to reflect METEOR's actual surface-oriented behavior.
  5. [Methods, Original Summaries in English; Abstract] The abstract draws conclusions about 'medical consultation summaries' generally, but the empirical base is exactly one simple and one complex fictitious summary per language. This single pair of source documents makes it impossible to separate system-level performance from document-specific effects, especially given the large variations in score across the two documents. While a pilot study may reasonably use a small sample, the conclusions should be explicitly limited to the two sample texts, and the Discussion should avoid generalizing the relative ranking of systems to other consultation summaries without further data.
minor comments (5)
  1. [Data Availability Statement] The Data Availability Statement says the data are 'available online via the link,' but no URL appears anywhere in the manuscript; this prevents independent verification and replication.
  2. [Throughout] The paper uses both 'CHR-F' and 'chrF' inconsistently; the original metric name is chrF (Popović, 2015), and the notation should be unified.
  3. [References] The reference list contains two entries labeled Wang et al. 2023a and Wang et al. 2023b with the same title 'Document-level machine translation with large language models'; this appears to be a duplicated reference.
  4. [Abstract] The phrase 'both patient, friendly and clinician, focused texts' contains a punctuation and spelling issue; it should be 'patient-friendly and clinician-focused texts.'
  5. [Figure 1] Figure 1 is described as presenting the comparative evaluation, but no full table of the 34 metric scores is provided; adding a numeric table would substantially improve the reproducibility and verifiability of the reported comparisons.

Circularity Check

1 steps flagged · score 1.0 of 10

No meaningful derivation-level circularity; the only self-referential element is the disclosed possible MT influence on reference translations, which mildly compromises benchmark independence.

  1. other [Methods – Reference Translations (page 3)]
    "However, it is common practice in the translation industry for many translators to use machine-generated drafts as a starting point. There are no regulations that prohibit the use of machine-generated translations, and ISO 17100 also incorporates guidelines for using machine-generated translations as preliminary drafts. Consequently, while every effort was made to ensure the quality and accuracy of the translations, some degree of influence from machine-generated drafts might still persist in the reference translations."

    The reference translations are the sole ground truth for BLEU, CHR-F, and METEOR. If those references were seeded by machine-generated drafts, as the passage admits is possible, then the metric scores measure, in part, similarity to the same class of traditional MT outputs being compared. The paper's central conclusion that traditional MT tools generally outperformed LLMs on surface-level metrics could therefore be partly self-referential: the scoring reference may itself be MT-influenced. No information is given on which engines, if any, seeded the references, so the direction and magnitude are unknown. The paper discloses the risk, and the professional post-editing and independent review mitigate it.

full rationale

This paper is an empirical translation evaluation, not a derivation. It compares outputs of three LLMs and three traditional MT tools against professional third-party reference translations using standard surface and semantic metrics. There is no mathematical derivation chain, no parameter fitted to a subset of data and then renamed a prediction, no ansatz smuggled in via citation, and no uniqueness theorem imported from the authors' prior work. The only self-referential element is the reference-translation process: the paper explicitly discloses that translators may have used machine-generated drafts as a starting point and that ISO 17100 permits this practice, so some influence from machine-generated drafts might persist in the references. Because BLEU, CHR-F, and METEOR all measure overlap with the reference, any such influence could bias scores toward traditional MT outputs that resemble the seeding drafts, slightly compromising the benchmark's independence. This is a validity concern rather than a constructional circularity: the metrics still compare against external human post-edited text, and the paper is transparent about the limitation. All other citations, including the authors' related Chen et al. 2024 study, serve as contextual motivation rather than load-bearing support for the empirical conclusion. The study is therefore essentially self-contained against an external benchmark, and the score is 1 only to reflect the disclosed reference-contamination caveat.

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

No free parameters are fitted; the paper's conclusions rest on three domain assumptions about the evaluation design. The main burden is the absence of independent ground truth beyond a single professional translation per condition.

assumptions (3)
  • domain assumption BLEU, CHR-F, and METEOR scores against a single reference are valid indicators of translation quality for ranking systems.
    The entire system ranking is built on these three metrics; the paper itself argues in the Discussion that they fail to capture clinical relevance, which weakens the assumed validity.
  • domain assumption The professional reference translations are ground truth, with only negligible contamination from machine-generated drafts.
    Stated in Methods: 'some degree of influence from machine-generated drafts might still persist in the reference translations.' If contamination is more than negligible, the benchmark is biased.
  • ad hoc to paper Two fictitious summaries represent the range of medical consultation summary complexity.
    The simple and complex summaries were designed by a palliative care expert for this study; no corpus or external validation shows they are representative of routine clinical documents.

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Cite this review

Pith. "Pith review of Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: A Pilot Study." pith.science (2026). https://pith.science/paper/Z5NKN5GA

@misc{pith2026250416601,
  author       = {Pith},
  title        = {Pith review of: Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: A Pilot Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z5NKN5GA}},
  note         = {Machine review of arXiv:2504.16601}
}
read the original abstract

This study evaluates how well large language models (LLMs) and traditional machine translation (MT) tools translate medical consultation summaries from English into Arabic, Chinese, and Vietnamese. It assesses both patient, friendly and clinician, focused texts using standard automated metrics. Results showed that traditional MT tools generally performed better, especially for complex texts, while LLMs showed promise, particularly in Vietnamese and Chinese, when translating simpler summaries. Arabic translations improved with complexity due to the language's morphology. Overall, while LLMs offer contextual flexibility, they remain inconsistent, and current evaluation metrics fail to capture clinical relevance. The study highlights the need for domain-specific training, improved evaluation methods, and human oversight in medical translation.

Figures

Figures reproduced from arXiv: 2504.16601 by the authors.

Figure 1
Figure 1. Translation performance comparison across three automatic evaluation metrics – BLEU, CHR-F, and METEOR – for three LLMs (GPT-4o, LLAMA-3.1, GEMMA-2) and three traditional MT tools (Google Translate represented by GTrans, Microsoft Bing Translator represented by MS, DeepL represented by DL). Subfigures (a–c) show results for the simple, patient-facing consultation summary translated into Arabic, Chinese, and Vietname… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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