arxiv: 2604.04723 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.AI

Recognition: 1 theorem link

· Lean Theorem

Individual and Combined Effects of English as a Second Language and Typos on LLM Performance

Serena Liu , Yutong Yang , Prisha Sheth , Weixuan Dong , Mingjiao Diao , Xinru Zhu , Nikhil Banga , Oscar Melendez

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Arnav Sharma Minda Zhao Marina Lin Mengyu Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:08 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords ESLtyposLLM evaluationlanguage variationerror robustnessperformance degradationclosed-ended tasks

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The pith

Combining English as a second language variations with typos leads to larger performance drops in large language models than either factor by itself.

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

The paper examines how non-native English speakers using LLMs often combine second-language variations with typing errors. It applies specific generation methods to create eight ESL variants and three levels of typos on standard inputs. Results show that the combination generally causes bigger declines in model performance than isolated effects, and this is not just the sum of individual impacts. The pattern holds more consistently for tasks with definite answers, while open-ended responses vary more. This indicates that testing models only on perfect standard English likely overestimates how well they work in everyday global use.

Core claim

When inputs include both ESL variations and typographical errors, LLMs show larger performance reductions than when facing either alone, though the combined impact is not simply additive, with the effect appearing most consistent on closed-ended tasks.

What carries the argument

Methods that transform standard English into eight English-as-second-language variants and inject typos at low, moderate, and severe levels to measure resulting performance changes.

Load-bearing premise

The eight ESL variants and three typo levels adequately represent the range of real non-native English inputs with errors that users actually produce.

What would settle it

Running the same tasks with authentic inputs collected from non-native English speakers and finding no greater combined performance drop or inconsistent patterns would challenge the findings.

Figures

Figures reproduced from arXiv: 2604.04723 by Arnav Sharma, Marina Lin, Mengyu Wang, Minda Zhao, Mingjiao Diao, Nikhil Banga, Oscar Melendez, Prisha Sheth, Serena Liu, Weixuan Dong, Xinru Zhu, Yutong Yang.

**Figure 2.** Figure 2: MMLU accuracy percentage organized by language and typo rate, per model. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: GSM8k accuracy percentage organized by language and typo rate, per model. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: HellaSwag accuracy percentage organized by language and typo rate, per model. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Dimension-wise degradation on MT-Bench under combined ESL and typographi [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

Large language models (LLMs) are used globally, and because much of their training data is in English, they typically perform best on English inputs. As a result, many non-native English speakers interact with them in English as a second language (ESL), and these inputs often contain typographical errors. Prior work has largely studied the effects of ESL variation and typographical errors separately, even though they often co-occur in real-world use. In this study, we use the Trans-EnV framework to transform standard English inputs into eight ESL variants and apply MulTypo to inject typos at three levels: low, moderate, and severe. We find that combining ESL variation and typos generally leads to larger performance drops than either factor alone, though the combined effect is not simply additive. This pattern is clearest on closed-ended tasks, where performance degradation can be characterized more consistently across ESL variants and typo levels, while results on open-ended tasks are more mixed. Overall, these findings suggest that evaluations on clean standard English may overestimate real-world model performance, and that evaluating ESL variation and typographical errors in isolation does not fully capture model behavior in realistic settings.

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

The paper shows non-additive drops from combining ESL variants and typos on LLMs, clearest on closed tasks, but the synthetic generators lack calibration to real non-native data.

read the letter

The key point is that ESL variations plus typos together hurt LLM performance more than either factor by itself, and the effect is not just additive. This shows up most consistently on closed-ended tasks. The authors use Trans-EnV to create eight ESL versions and MulTypo for three typo intensities, then test across task types. Prior work handled these issues separately, so measuring the interaction is the actual new piece here. The experiment is set up cleanly enough to make the directional claim believable on its own terms. The paper does a decent job flagging that clean-English benchmarks probably overstate real-world results for many users. The main limitation is that the ESL and typo generators are not validated against actual learner corpora or real usage logs. Without that check, it's possible the observed interaction pattern depends on how these particular pipelines happen to distribute errors rather than on how non-native speakers actually write. The abstract gives no numbers, error bars, or model details, so the size of the effect and its consistency across models remain unclear from the summary. This work is aimed at people building or evaluating LLMs for global use. Anyone running robustness tests or thinking about deployment outside native English will find the setup useful as a starting point, even if they need to add their own real-data checks. It is worth sending to peer review because the question is practical and the design is replicable, but referees will need to press on the validation gap and demand the full quantitative results.

Referee Report

2 major / 0 minor

Summary. The paper examines the individual and combined effects of English as a Second Language (ESL) variations and typographical errors on LLM performance. It uses the Trans-EnV framework to generate eight ESL variants from standard English inputs and applies MulTypo to inject typos at low, moderate, and severe levels. The central claim is that combining ESL variation and typos produces larger performance drops than either factor alone (though not simply additive), with this pattern clearest and most consistent on closed-ended tasks; open-ended tasks yield more mixed results. The authors conclude that evaluations on clean standard English overestimate real-world performance and that isolated studies of each factor are insufficient.

Significance. If the empirical patterns hold after addressing validation concerns, the work is significant for LLM evaluation practices in NLP. It provides evidence that interaction effects between common real-world input perturbations matter, which could inform more realistic benchmarks, robustness testing, and model development. The study builds on prior separate examinations of ESL and typos by directly testing their combination, highlighting a gap in current evaluation protocols.

major comments (2)

The experimental setup (as described in the abstract and methods) relies on Trans-EnV and MulTypo to produce the eight ESL variants and three typo levels, but provides no calibration or comparison against real non-native learner corpora (e.g., error-type frequencies, syntactic patterns, or co-occurrence rates of grammatical and typographical mistakes from sources like the Cambridge Learner Corpus or TOEFL data). This assumption is load-bearing for the central claim of non-additive combined effects and the suggestion that clean-English evaluations overestimate performance; without it, the observed patterns risk being artifacts of the generators rather than generalizable findings.
Results section: The abstract reports only directional findings (larger drops, non-additive, clearest on closed-ended tasks) without quantitative metrics such as exact performance deltas, error bars, statistical significance tests, or details on model selection, task distribution, number of runs, or variance across ESL variants. This limits assessment of the magnitude and reliability of the interaction effect, which is central to the paper's conclusions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of validation and reporting. We address each major comment below and describe the revisions we will undertake.

read point-by-point responses

Referee: The experimental setup (as described in the abstract and methods) relies on Trans-EnV and MulTypo to produce the eight ESL variants and three typo levels, but provides no calibration or comparison against real non-native learner corpora (e.g., error-type frequencies, syntactic patterns, or co-occurrence rates of grammatical and typographical mistakes from sources like the Cambridge Learner Corpus or TOEFL data). This assumption is load-bearing for the central claim of non-additive combined effects and the suggestion that clean-English evaluations overestimate performance; without it, the observed patterns risk being artifacts of the generators rather than generalizable findings.

Authors: We agree that the absence of direct quantitative calibration against real learner corpora such as the Cambridge Learner Corpus represents a limitation for claims of generalizability. The Trans-EnV and MulTypo frameworks were selected as established tools from prior work for controlled generation of ESL variants and typos. In the revised manuscript, we will add a dedicated subsection in the Methods or Limitations section that details the generation parameters, qualitatively compares observed error patterns to known characteristics of learner data where feasible, and explicitly states this as a limitation while recommending future validation against corpus resources. This will clarify the scope of our findings without requiring new experiments. revision: partial
Referee: Results section: The abstract reports only directional findings (larger drops, non-additive, clearest on closed-ended tasks) without quantitative metrics such as exact performance deltas, error bars, statistical significance tests, or details on model selection, task distribution, number of runs, or variance across ESL variants. This limits assessment of the magnitude and reliability of the interaction effect, which is central to the paper's conclusions.

Authors: We acknowledge that the current abstract and results presentation emphasize directional patterns over detailed quantitative reporting. To improve transparency and allow evaluation of effect magnitudes and reliability, we will revise the abstract to incorporate key quantitative examples (e.g., average performance drops and interaction patterns). We will also expand the results section to report exact deltas, include error bars or variance measures, specify the number of runs, detail model selection and task distribution, and add statistical significance tests for the non-additive effects. These changes will directly address the concern about assessing the interaction effect. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical measurement study

full rationale

The paper conducts an empirical evaluation by applying the Trans-EnV framework to generate eight ESL variants and MulTypo to inject three levels of typos, then measuring LLM performance drops on closed- and open-ended tasks. The central claim—that combined ESL+typos produce larger non-additive drops, clearest on closed-ended tasks—is a direct observational result from these experiments, not a quantity derived from or defined in terms of itself. No equations, fitted parameters, predictions, uniqueness theorems, or ansatzes appear in the provided text; prior work is referenced only descriptively without load-bearing self-citations that reduce the findings to tautology. The derivation chain is empty because none exists.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on the assumption that the chosen transformation frameworks produce inputs that are ecologically valid for real non-native users; no free parameters or invented entities are introduced.

axioms (1)

domain assumption Trans-EnV and MulTypo produce representative ESL variants and typo patterns
Invoked when the authors apply the frameworks to generate test inputs and interpret results as reflecting real-world conditions.

pith-pipeline@v0.9.0 · 5544 in / 1141 out tokens · 37635 ms · 2026-05-10T19:08:43.802443+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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unclear
Relation between the paper passage and the cited Recognition theorem.

We use Trans-EnV to transform standard English inputs into eight ESL variants and apply MulTypo to inject typos at three levels

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

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