[2604.04723] Individual and Combined Effects of English as a Second Language and Typos on LLM Performance

Computer Science > Computation and Language arXiv:2604.04723 (cs) [Submitted on 6 Apr 2026] Title:Individual and Combined Effects of English as a Second Language and Typos on LLM Performance Authors:Serena Liu, Yutong Yang, Prisha Sheth, Weixuan Dong, Mingjiao Diao, Xinru Zhu, Nikhil Banga, Oscar Melendez, Arnav Sharma, Minda Zhao, Marina Lin, Mengyu Wang View a PDF of the paper titled Individual and Combined Effects of English as a Second Language and Typos on LLM Performance, by Serena Liu and 11 other authors View PDF HTML (experimental) 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 varia...