Papers with non-Latin script
Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek (2026.acl-srw)
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| Challenge: | a recent study found that large language models are biased overwhelmingly Anglocentric . a stereotype perceptual map is a framework for analyzing how ethnic groups are positioned along evaluative dimensions. |
| Approach: | They use a stereotype perceptual map to examine how ethnic groups are positioned along evaluative dimensions. |
| Outcome: | The stereotype perceptual map analyzes model behavior across languages, scripts, evaluative domains and models. |
Nonparametric Masked Language Modeling (2023.findings-acl)
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| Challenge: | Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. |
| Approach: | They introduce a nonparametric masked language model that replaces a softmax with a distribution over every phrase in a reference corpus and uses an in-batch approximation to train it. |
| Outcome: | The proposed model outperforms larger parametric models on 16 tasks including classification, fact probing and question answering. |
Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) have been primarily focused on English, leaving the multilingual ability unexplored. |
| Approach: | They propose a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation. |
| Outcome: | The proposed method is light-weight and performant but has limitations for older models and high resource languages. |
Prompting with Phonemes: Enhancing LLMs’ Multilinguality for Non-Latin Script Languages (2025.naacl-long)
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Hoang H Nguyen, Khyati Mahajan, Vikas Yadav, Julian Salazar, Philip S. Yu, Masoud Hashemi, Rishabh Maheshwary
| Challenge: | Multilingual LLMs have achieved remarkable benchmark performance, but continue to underperform on non-Latin script languages. |
| Approach: | They propose to integrate phonemic transcriptions as complementary signals to induce script-invariant representations by integrating phonemic and orthographic transcriptions. |
| Outcome: | The proposed approach improves performance for Latin and non-Latin script languages, with 12.6% performance improvement and 15.1% performance improvement compared to randomized ICL retrieval. |
Phonetic and Visual Priors for Decipherment of Informal Romanization (2020.acl-main)
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| Challenge: | Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. |
| Approach: | They propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. |
| Outcome: | The proposed model improves on romanized Egyptian Arabic and Russian data and is closer to the supervised skyline. |
How Does Quantization Affect Multilingual LLMs? (2024.findings-emnlp)
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Kelly Marchisio, Saurabh Dash, Hongyu Chen, Dennis Aumiller, Ahmet Üstün, Sara Hooker, Sebastian Ruder
| Challenge: | Quantization is widely used to improve inference speed and deployment of large language models. |
| Approach: | They conduct a thorough analysis of quantized multilingual LLMs . they find language disparately affected by quantization, non-Latin script languages worst . authors urge consideration of multilingual performance as evaluation criterion for efficient models . |
| Outcome: | The results show that quantization has harmful effects on human evaluation . language performance is disparately affected by quantization, the authors say . |