arxiv: 2210.03057 · v1 · submitted 2022-10-06 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

Language Models are Multilingual Chain-of-Thought Reasoners

Freda Shi , Mirac Suzgun , Markus Freitag , Xuezhi Wang , Suraj Srivats , Soroush Vosoughi , Hyung Won Chung , Yi Tay

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Sebastian Ruder Denny Zhou Dipanjan Das Jason Wei

Authors on Pith no claims yet

Pith reviewed 2026-05-15 21:02 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords multilingual reasoningchain-of-thought promptinglanguage modelsMGSM benchmarkgrade school mathmodel scalingcross-lingual transfer

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The pith

Large language models gain step-by-step reasoning ability across many languages as they scale up.

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

The paper creates the MGSM benchmark by translating 250 grade-school math problems into ten languages and tests whether chain-of-thought prompting elicits correct solutions. Performance rises sharply once models pass a size threshold, and the gains appear even in languages with limited training data such as Bengali and Swahili. The same prompting method also improves results on commonsense reasoning and word-in-context tasks. A reader should care because the result indicates that current models can handle structured reasoning without language-specific training or data.

Core claim

The ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and language models exhibit strong multilingual reasoning abilities even in underrepresented languages such as Bengali and Swahili.

What carries the argument

The Multilingual Grade School Math (MGSM) benchmark, formed by manual translation of GSM8K problems into ten typologically diverse languages, which measures whether chain-of-thought prompting produces correct step-by-step solutions outside English.

Load-bearing premise

The manual translations keep the original logical structure, difficulty, and meaning of each problem without introducing artifacts that change how hard the task is in the new language.

What would settle it

A new model series that shows flat or declining accuracy on the non-English MGSM sets as parameter count grows, while English accuracy continues to rise.

read the original abstract

We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and that models have strikingly strong multilingual reasoning abilities, even in underrepresented languages such as Bengali and Swahili. Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and word-in-context semantic judgment. The MGSM benchmark is publicly available at https://github.com/google-research/url-nlp.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Chain-of-thought reasoning emerges with scale in large models across ten languages on a new translated math benchmark.

read the letter

The main takeaway is that chain-of-thought prompting lets large models solve grade-school math problems in languages beyond English, including low-resource ones like Bengali and Swahili, and this ability appears as models grow. The authors created MGSM by manually translating 250 GSM8K problems into ten typologically diverse languages and ran the usual CoT experiments on it. They also checked the pattern on commonsense reasoning and word-in-context tasks. The data release at the GitHub link is a clear positive for anyone who wants to build on the work. What is actually new is the multilingual benchmark itself and the documentation of how CoT performance scales outside English. Prior CoT papers stayed mostly in English, so this extends the empirical picture in a straightforward way. The scaling curves and cross-language comparisons are presented clearly enough to see the pattern. The soft spot is the translation process. The abstract gives no information on translator background, back-translation checks, or any quantitative test that the problems kept the same difficulty and logical structure. If any language versions ended up easier or harder because of phrasing choices, that could shift the apparent performance gaps. That part feels under-specified relative to how much the claims rest on it. This paper is useful for researchers working on multilingual LLMs or on reasoning benchmarks that need to cover more than English. Someone evaluating models for education or problem-solving applications in non-English settings would find the scaling results and the public dataset practical. The core empirical observation looks solid enough to deserve referee time, even if the translation details need more attention in revision. I would send it for peer review.

Referee Report

2 major / 2 minor

Summary. The paper introduces the Multilingual Grade School Math (MGSM) benchmark by manually translating 250 grade-school math problems from GSM8K into ten typologically diverse languages. It evaluates large language models using chain-of-thought prompting, finding that multilingual reasoning ability emerges with increasing model scale and remains strong even in low-resource languages such as Bengali and Swahili. The evaluation is extended to commonsense reasoning and word-in-context tasks, with the benchmark released publicly.

Significance. If the central empirical results hold, the work provides concrete evidence that chain-of-thought reasoning generalizes across languages in a scale-dependent manner, moving beyond English-centric evaluations of LLMs. The public benchmark release supports reproducibility and future multilingual research.

major comments (2)

[§3] §3 (MGSM benchmark construction): The manual translation process is described only at a high level. No information is provided on translator qualifications, use of back-translation or other verification steps, or quantitative checks (e.g., difficulty metrics or semantic similarity scores) to confirm that translated problems preserve original logical structure and difficulty. This assumption is load-bearing for claims of strong reasoning in underrepresented languages.
[§4-5] Experimental details (throughout §4 and §5): Exact model versions, full prompting templates per language, and statistical significance tests for cross-language and cross-scale differences are not reported. These omissions limit the ability to interpret the magnitude and reliability of the reported multilingual performance.

minor comments (2)

[Abstract] Abstract: The abstract could explicitly state the ten languages evaluated to better foreground the typological diversity.
[Results] Figures and tables: Ensure consistent labeling of languages and model sizes across all plots and tables for immediate readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive recommendation of minor revision and the constructive comments. We appreciate the emphasis on transparency in benchmark construction and experimental reporting. We address each major comment below and will make the indicated revisions to strengthen the manuscript.

read point-by-point responses

Referee: [§3] §3 (MGSM benchmark construction): The manual translation process is described only at a high level. No information is provided on translator qualifications, use of back-translation or other verification steps, or quantitative checks (e.g., difficulty metrics or semantic similarity scores) to confirm that translated problems preserve original logical structure and difficulty. This assumption is load-bearing for claims of strong reasoning in underrepresented languages.

Authors: We agree that additional details would improve clarity and address potential concerns about translation fidelity. In the revised manuscript, we will expand the description in §3 to specify that translations were carried out by professional translators who are native speakers of each target language and highly proficient in English. We will describe a verification process that included back-translation of a random subset of problems to English, followed by manual review against the originals by the authors to confirm preservation of logical structure and numerical content. We will also note that no quantitative semantic similarity metrics were computed because the problems are short, direct translations with no alterations to meaning or difficulty; the benchmark design intentionally isolates language while keeping the underlying math identical to GSM8K. These additions will be added without changing any results or claims. revision: yes
Referee: [§4-5] Experimental details (throughout §4 and §5): Exact model versions, full prompting templates per language, and statistical significance tests for cross-language and cross-scale differences are not reported. These omissions limit the ability to interpret the magnitude and reliability of the reported multilingual performance.

Authors: We thank the referee for highlighting these omissions. In the revised version, we will add the following: (1) explicit model versions and parameter counts (e.g., PaLM 8B, 62B, and 540B as released in the original PaLM work); (2) the complete set of prompting templates for all ten languages in a new appendix, with a note that templates were minimally adapted from the English version to maintain semantic equivalence; (3) statistical significance testing (paired bootstrap or t-tests with reported p-values) for the main cross-language and cross-scale comparisons in §4 and §5. These changes will be incorporated into the text and appendix without altering any empirical findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical benchmark evaluation

full rationale

The paper introduces the MGSM benchmark via manual translation of 250 GSM8K problems into ten languages and reports empirical results on chain-of-thought performance across model scales. No derivations, equations, fitted parameters, or predictions are present that reduce reported outcomes to inputs by construction. The central claims rest on direct evaluation rather than any self-referential loop, self-citation load-bearing premise, or ansatz smuggling. External citation to Cobbe et al. (2021) for the source dataset is non-circular as it supplies independent data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that chain-of-thought prompting reliably elicits reasoning and that the translated problems test the same underlying capability as the English originals. No free parameters are fitted to produce the reported scaling trend.

axioms (2)

domain assumption Chain-of-thought prompting elicits step-by-step reasoning in large language models
Invoked to interpret the performance gains observed with model scale.
domain assumption Manual translations preserve problem difficulty and logical structure across languages
Required for the claim that strong performance reflects genuine multilingual reasoning rather than translation artifacts.

pith-pipeline@v0.9.0 · 5463 in / 1286 out tokens · 82737 ms · 2026-05-15T21:02:25.175118+00:00 · methodology

discussion (0)

Forward citations

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