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Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A single open 9B-parameter language model, Gemma 9B, outperforms 16 other models up to 9B parameters at correcting grammar across English, German, Italian, and Swedish in a reference-free evaluation.

desk verdict A transparent, useful multilingual GEC benchmark, but the 'Gemma 9B is best' claim flips without the LanguageTool column; solid work that needs a softer conclusion. read the letter →

arxiv 2505.06004 v1 pith:S2LPMTMH submitted 2025-05-09 cs.CL

Dawid Wisniewski , Antoni Solarski , Artur Nowakowski This is my paper · ORCID
classification cs.CL
keywords multilingualgrammaticalerrorcorrectionlargelanguagemodelsreference-freeevaluationToolzero-shotpromptingBordaaggregationdriftGemma9B
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 paper asks whether one moderately sized open language model can correct grammar in several languages at once, and which of 17 models up to 9B parameters does that best for English, German, Italian, and Swedish. Using a reference-free protocol that measures grammatical correctness, meaning preservation, edit minimality, preservation of already-correct sentences, and language fidelity, the authors find that Gemma 9B comes out on top overall, with Gemma 2B, EuroLLM 9B, OpenChat 3.5, EuroLLM 1.7B, and Llama 3.1 also improving grammatical correctness in all four languages. They also show that a longer, more specific prompt that asks for minimal changes and explicitly tells the model to return correct texts unchanged wins in 32 of 36 metric-language scenarios. The results matter because they suggest a single consumer-GPU-sized model can replace per-language GEC systems, and they expose language drift as the main failure mode of smaller multilingual LLMs.

What carries the argument

The load-bearing mechanism is a reference-free evaluation scheme built from eight metrics organized into five requirements. Grammatical correctness is scored as $1/(1+\text{errors})$ with LanguageTool error counts; semantic preservation uses BERTScore, BLEURT, and SentenceBERT; minimal edits use Levenshtein distance, GLEU, and token-length difference; correct-sentence preservation uses an F1 score over sentences originally marked correct; and language fidelity uses a FastText-based language-identification drift score. Per-metric rankings are fused with a two-stage Borda aggregation, a voting-style method that assigns points by rank position, giving equal weight to correctness, semantics, syntax, and text preservation. The scheme converts raw model outputs into a defensible ordering and is what the ranking claims stand on.

What would settle it

Re-evaluate a random sample of the same four-language outputs with human annotators counting grammar errors directly (or using MultiGED's token-level gold annotations instead of LanguageTool); if the corrected-error rates put a different model on top, or if fewer than six models improve in all four languages, the central claim collapses.

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Extended reading notes

Core claim

The central claim is that among 17 open LLMs of up to 9B parameters, Gemma 9B is currently the best overall multilingual grammatical error corrector for English, German, Italian, and Swedish. It ranks first on the language-averaged grammatical-correctness score (LanguageTool 0.948), first on two of the three semantic preservation metrics (BERTScore 0.937, SentenceBERT 0.954), first on length difference (0.942) and on keeping already-correct sentences unchanged (F1 0.560), while avoiding language drift in all four languages. Six models -- Gemma 9B, Gemma 2B, EuroLLM 9B, EuroLLM 1.7B, OpenChat 3.5, and Llama 3.1 -- improve grammatical correctness in every one of the four languages, whereas eight models fail to support all four languages at all and frequently fall back to English. The paper also claims that the longest, most concrete prompt (ask for minimal changes, return only corrected text, do not touch correct inputs) is the best in 32 of 36 metric-language scenarios, primarily because it substantially raises the score for preserving correct sentences.

Load-bearing premise

The ranking rests on LanguageTool's error counts being an equally reliable measure of grammatical correctness across English, German, Italian, and Swedish; if it systematically misses, invents, or weights error types differently in any of these languages, the ranking measures the wrong target.

Editorial extensions

If this is right

  • A single open ~9B model can serve as the GEC backend for English, German, Italian, and Swedish, removing the need for per-language models or paid APIs.
  • Prompt design can substitute for fine-tuning: the longest, most concrete prompt (P3) lifted the average F1 for keeping correct sentences from roughly 0.12 to 0.34 on English and by similar margins on other languages.
  • Task-specific fine-tuning works: the Karen models, fine-tuned from Mistral, beat their base model on English while making the smallest edits.
  • Training data and distillation matter more than raw parameter count: Gemma 2B and EuroLLM 1.7B support all four languages while larger models such as BLOOM and SmolLM do not.

Reading between the lines

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

  • The equal-weight Borda aggregation is a modeling choice; if a user weights raw error reduction above all else, Qwen 2.5 and Aya become competitive or better per language, so 'best' is partly policy-dependent.
  • The same protocol could be ported to other language triples or quadruples (e.g., Spanish, French, Portuguese) to test whether Gemma 9B's lead is a Germanic/Romance artifact or a general property of the model.
  • Because LanguageTool misses certain error types, the six-model list that 'improves grammatical correctness' is a lower bound; a checker with wider coverage might certify additional models such as Aya or Qwen on Swedish.
  • The P3 prompt's explicit 'return correct text unchanged' instruction appears to be the main driver of the F1 gains; varying only that clause while keeping prompt length fixed would isolate whether it is the instruction or the length that matters.
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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

3 major / 5 minor

Summary. The paper reports a systematic comparison of 17 open-weight LLMs with up to 9B parameters for multilingual grammatical error correction (GEC) on English, German, Italian, and Swedish. Using the MultiGED dev set, it computes reference-less metrics for five requirements: LanguageTool-based grammatical correctness, semantic similarity (BERTScore, BLEURT, SentenceBERT), syntactic minimality (Levenshtein, GLEU, length difference), preservation of correct sentences (F1), and language drift. The authors select the longest and most concrete prompt (P3) by comparing three prompts on the same dev data, then aggregate per-metric rankings with a two-level Borda procedure. They conclude that six models improve LanguageTool-scored correctness in all four languages and that Gemma 9B is currently the best overall model; they also discuss model-size effects, recurring failure modes, and release the generated outputs publicly.

Significance. If the claims hold, this is a useful practical benchmark: it covers a wide and current model set, uses a coherent multi-criteria reference-less protocol, performs a stability check for two models, and publicly releases the generated outputs, which supports reproducibility. The internally consistent ranking and the authors' explicit Limitations section are strengths. However, the significance is bounded by the fact that the headline 'best model' conclusion is an empirical measurement claim whose validity depends on LanguageTool being a language-fair correctness measure and on the absence of selection effects in the prompt choice; both of these are load-bearing concerns rather than presentation issues. The strongest assets are the public data release and the explicit decomposition of quality into semantic, syntactic, correctness, and preservation perspectives.

major comments (3)
  1. [Section 4.3 (Req. 1) and Tables 5-6] The conclusion that Gemma 9B is 'the best model overall for the GEC task' (Section 6) rests on the LanguageTool error count being a language-fair measure of grammatical correctness. Table 6 shows that Gemma 9B is 1st on LT and tied-1st on the semantic perspective, but only 9th on Levenshtein, while Qwen 2.5 is 8th on LT, tied-1st on the semantic perspective, and 1st on several syntactic metrics; the LT rank gap is the main separator in the Borda aggregation. A sensitivity check using Table 6 shows that removing the LT perspective leaves the two models roughly tied on the remaining perspectives, or puts Qwen ahead if the F1 preservation perspective is also excluded. The Limitations concede that LT 'may introduce some bias towards errors detected by LanguageTool,' and no per-language precision/recall analysis of LT, no human evaluation, and no gold-reference comparison are reported. I request per-language LT validation (for example, against MultiGED token-level labels) or a human-annotated sample, together with a sensitivity analysis of the Borda ranking when the LT perspective is removed or reweighted.
  2. [Section 5 (Prompt selection) and Tables 3-5] The best prompt (P3) is selected by comparing P1-P3 on the same MultiGED dev split that is then used for all subsequent model ranking and for answering RQ3. This is a selection-on-the-evaluation-set design: the reported advantage of P3, and potentially the final model ranking, are optimistically biased because the evaluation data were used to choose the prompt. The paper should hold out a test portion of MultiGED for the final evaluation, or at minimum report the model ranking under P1 and P2 and state explicitly whether the conclusions are invariant to the prompt choice.
  3. [Section 6 (Result stability) and Tables 4-5] The stability analysis is too thin to support the precision of the reported ranking. It checks only two of the seventeen models, reports only the LanguageTool score for run-to-run variation, and provides no confidence intervals or significance tests on the Borda totals. Table 5 has several adjacent models with close aggregate ranks, so the final ordering may not be distinguishable from noise. The paper should provide bootstrap intervals on the Borda scores, or at least per-model run-to-run variance for all constituent metrics, and state explicitly which adjacent rank differences are larger than the measurement noise.
minor comments (5)
  1. [Section 6 and Section 8] There are typos in the prose: 'ranked foruth overall' should be 'ranked fourth overall', and 'an par with' should be 'on a par with'.
  2. [Section 5 and Table 8] The caption of Table 8 says the prompt-quality analysis is 'averaged over all 7 models supporting all four languages considered,' but the text lists nine supporting models (Aya, EuroLLM 1.7B, EuroLLM 9B, Gemma 2B, Gemma 9B, Llama 3.1, OpenChat 3.5, Qwen 2.5, and Yi). This inconsistency should be reconciled.
  3. [Section 4.2] The sentence 'The parameter selection is inspired by Karen 2, which is the only model (considered) fine-tuned for GEC' is inaccurate because both Karen-strict and Karen-creative are fine-tuned for GEC; please rephrase to refer to the Karen models.
  4. [Section 4.3 (Req. 1)] The specific LanguageTool wrapper version and configuration used are not reported; specifying them would improve reproducibility.
  5. [Section 5] The phrase 'selected the best performing prompt, and used it to verify which LLMs support all the languages' should say 'evaluate' rather than 'verify', since the prompt is part of the evaluation protocol rather than an external ground truth.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the ranking is a direct empirical comparison using external metrics, with no fitted parameters or load-bearing self-citation.

full rationale

The paper's central claim that Gemma 9B is the best overall model for multilingual GEC is a ranking computed from externally defined metrics (LanguageTool error counts, BERTScore, SentenceBERT, BLEURT, Levenshtein, GLEU, length difference, and Correct-F1) applied to MultiGED dev-set sentences and LLM outputs. No parameter is fitted to the target conclusion, no prediction is derived from a fitted input, and no result is imported from the authors' own prior work. The prompt-selection step chooses P3 because it performs best on aggregate scores, which is a methodological choice, not a construction that forces the final model ranking. The Borda aggregation is a transparent, hand-specified weighting of four perspectives; the 'best model' claim is relative to that evaluation framework, but that is not circular. The acknowledged limitation that LanguageTool may bias error counts is a measurement-validity concern, not a circularity concern, because the metric is not defined in terms of the conclusion. No self-citation, uniqueness theorem, or ansatz-smuggling chain appears in the derivation.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on automatic proxies, especially LanguageTool, correctly ranking grammar quality, and on the authors' choices of prompts, decoding settings, and Borda weights. All are stated transparently, but none are externally validated against human judgments, which contributes to the CONDITIONAL verdict.

free parameters (2)
  • Generation hyperparameters = top_k=40, top_p=0.1, repetition_penalty=1.18, max_new_tokens=256, do_sample=True
    Chosen from the Karen GEC model's suggested settings and shared across all 17 models. They may interact with model training to affect absolute quality, but they are applied uniformly and are not fitted to the target ranking.
  • Borda aggregation weights = Equal weights for 4 perspectives: LT, F1, semantic group, syntactic group
    The two-step Borda design gives each of the four perspectives equal influence on the final rank. Different weights would change the ranking; for example, Qwen 2.5 is strong on semantic and syntactic similarity but fails to raise the Swedish LT score, while Gemma 9B is balanced across perspectives.
assumptions (5)
  • domain assumption LanguageTool error counts are a valid language-fair proxy for grammatical correctness in EN, DE, IT, SV.
    Used in Requirement 1 and as the main correctness signal for ranking. The paper's Limitations note LT may be biased toward errors it recognizes; if LT is unfair across languages, the 'improves grammatical correctness' claim and the ranking weaken.
  • domain assumption Sentence-level correctness labels from MultiGED, derived by detokenizing token-level GED annotations, are accurate enough to compute the F1 'keep correct sentences unchanged' metric.
    Section 3 describes converting token annotations to sentence labels by marking a sentence incorrect if any token is incorrect. Annotation noise in MultiGED would propagate into the F1 values used to compare prompts and models.
  • domain assumption The three tested prompts represent a meaningful range of practical GEC instructions, and selecting the best one on the same dev set used for final ranking is acceptable.
    Prompt choice is a key experimental variable. The paper selects P3 based on averaged performance on the same MultiGED dev set that later produces the model ranking, introducing selection on the evaluation set.
  • domain assumption The three semantic similarity metrics (BERTScore, SentenceBERT, BLEURT) and three syntactic metrics (Levenshtein, length difference, GLEU), with the specified multilingual models, capture meaning preservation and minimal-change requirements.
    Used in Requirements 2 and 3. No validation is provided that these automatic metrics agree with human judgments of meaning preservation for these four languages.
  • domain assumption Borda rank aggregation with the described grouping yields a meaningful single-model ranking.
    The rank aggregation scheme in Section 4.3 assigns equal weight to four perspectives; this is a transparent design choice not derived from theory or an external benchmark.

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Pith. "Pith review of Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models." pith.science (2026). https://pith.science/paper/S2LPMTMH

@misc{pith2026250506004,
  author       = {Pith},
  title        = {Pith review of: Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S2LPMTMH}},
  note         = {Machine review of arXiv:2505.06004}
}
read the original abstract

Recent language models can successfully solve various language-related tasks, and many understand inputs stated in different languages. In this paper, we explore the performance of 17 popular models used to correct grammatical issues in texts stated in English, German, Italian, and Swedish when using a single model to correct texts in all those languages. We analyze the outputs generated by these models, focusing on decreasing the number of grammatical errors while keeping the changes small. The conclusions drawn help us understand what problems occur among those models and which models can be recommended for multilingual grammatical error correction tasks. We list six models that improve grammatical correctness in all four languages and show that Gemma 9B is currently the best performing one for the languages considered.

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    " 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 gl...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.