Challenge: Prior studies show that large language models map multilingual content into English-aligned representations at intermediate layers before projecting them back into target-language token spaces in the later layers.
Approach: They propose a method to identify and manipulate dimensions that are sparse and sparsity-based . they propose to use as few as 50 sentences of either parallel or monolingual data to manipulate these dimensions .
Outcome: Experiments on a multilingual generation control task show the interpretability of these dimensions.

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The Geometry of Multilingual Language Model Representations (2022.emnlp-main)

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Challenge: XLM-R models encode language-sensitive information in each language, allowing them to extract features for downstream tasks and cross-lingual transfer learning.
Approach: They evaluate how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language.
Outcome: The proposed model can extract features for downstream tasks and cross-lingual transfer learning.
Language Directions in Multilingual LLMs: A Layer-wise Diagnostic Study of Token Alignment and Pretraining Imprint (2026.acl-srw)

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Challenge: Using a unified probing framework, we analyze six multilingual LLMs across five languages.
Approach: They analyze multilingual representations across five languages and analyze their behavior . they find that accuracy rises by +73.5 to +80.7 points from L0 to L1 on average .
Outcome: The proposed framework enables a consistent and substantial early jump in accuracy across models . the token–language alignment measures where vocabulary sharing peaks .
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models (2025.findings-naacl)

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Challenge: Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models.
Approach: They investigate the impact of parallel corpora quality and quantity, training objectives, and model size on performance of multilingual large language models enhanced with parallel corporeal.
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Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages.
Approach: They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods.
Outcome: The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs.
Explainability and Interpretability of Multilingual Large Language Models: A Survey (2025.emnlp-main)

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Challenge: Existing literature on multilingual large language models lacks transparency in their internal processes.
Approach: They propose to use multilingual large language models to examine their explainability and interpretability methods.
Outcome: The present study examines the explainability and interpretability of multilingual large language models.
Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)

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Challenge: Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages.
Approach: They propose to compare large language models with state-of-the-start finetuned smaller models to find that large language model controls are comparable to smaller models.
Outcome: The proposed model can meet hard constraints and perform better than state-of-the-art models.
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model (2025.acl-long)

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Challenge: Existing models for large vision-language tasks are trained on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language.
Approach: They conduct multi-stage experiments on 13 vision-language tasks and 43 languages . they find that one can include as many as 100 training languages simultaneously with as little as 25-50% of non-English data .
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Beyond Monolingual Assumptions: A Survey on Code-Switched NLP in the Era of Large Language Models across Modalities (2026.acl-long)

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Challenge: Amidst the rapid advances of large language models, most LLMs struggle with mixed-language inputs, limited Code-switching datasets, and evaluation biases.
Approach: They propose a roadmap for inclusive datasets, fair evaluation, and linguistically grounded models to achieve truly multilingual intelligence.
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An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)

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Challenge: In this study, we explore massively multilingual low-resource neural machine translation.
Approach: They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages.
Outcome: The proposed approach is highly language-specific and can be tailored to the source language and its typology.
The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining (2026.acl-long)

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Challenge: Existing research suggests that multilingual large language models can achieve impressive cross-lingual understanding despite largely monolingual pretraining.
Approach: They compare a monolingual-only corpus with a standard web corpus that removes all multilingual documents and then retrain the models from scratch under controlled conditions.
Outcome: The results show that removing bilingual data causes translation performance to drop 56% in BLEU, whereas code-switching contributes minimally.

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