Challenge: Recent studies have focused on enhancing multilingual large language models (MLLMs) for specific languages.
Approach: They propose an efficient layer-specific optimization method to enhance continual pretraining (CP) for specific languages in multilingual large language models (MLLMs).
Outcome: The proposed method achieves a training speedup of up to 6.46 times compared to existing methods while improving target language performance by up to 5.2% on qualitative benchmarks.

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Efficient Continual Pre-training of LLMs for Low-resource Languages (2025.naacl-industry)

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Challenge: Open-source large language models (LLMs) are a promising tool for low-resource languages . however, there is still a substantial performance gap between high-resourced languages and LRLs .
Approach: They develop an algorithm to select a subset of texts from a larger corpus and use it to select tokens for LLMs.
Outcome: The proposed algorithm reduces the cost of continual pre-training (CPT) with large amounts of language-specific data.
LaCo: Layer-wise Compensation for Pruned Large Language Models (2026.acl-long)

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Challenge: Existing methods for predicting performance degradations of Large Language Models (LLMs) neglect the structural distortions caused by sparsity.
Approach: They propose a framework that reorients the recovery paradigm from global adaptation to hierarchical representation alignment by sequentially optimizing each layer to reconstruct the model's hidden states.
Outcome: The proposed framework surpasses parameter-efficient baselines in perplexity reduction and zero-shot reasoning.
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)

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Challenge: Large language models (LLMs) use pretraining to predict the subsequent word, but less-resourced languages are being overlooked.
Approach: They propose to expand the MLLM vocabularies to enhance expressiveness and use bilingual data for pretraining to align the high- and less-resourced languages.
Outcome: The proposed model outperforms existing models in qualitative analyses compared to Korean monolingual models.
Exploring Design Choices for Building Language-Specific LLMs (2024.findings-emnlp)

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Challenge: Prior work focused on building multilingual models that cover a broad spectrum of languages.
Approach: They conduct systematic experiments on how design choices impact the adapted LLM, both in terms of efficiency and end task performance.
Outcome: The proposed model performs better on English-centric models than multilingual models despite poor performance on low-resource languages.
ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining (2025.emnlp-industry)

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Challenge: Domain Adaptive Continual Pretraining (DACP) is a method to mitigate performance degradation in small LLMs and enhance their effectiveness in target domains.
Approach: They propose a continual pretraining methodology that optimizes sLLMs within service domains and enhances their effectiveness in target domains.
Outcome: The proposed model achieves significant gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)

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Challenge: Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks.
Approach: They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance.
Outcome: The proposed model can adapt to new corpora while retaining knowledge in earlier domains.
Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive.
Approach: They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them.
Outcome: The proposed approach saves significant resources and accelerates convergence and performance.
mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model (2021.findings-emnlp)

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Challenge: Existing domain-specific multilingual pretraining data is difficult to obtain due to regulations, legislation, or simply a lack of language- and domain- specific text.
Approach: They propose to continue pretraining a language model on domain-specific unlabelled text . this allows for better modelling of text for downstream tasks within the domain .
Outcome: The proposed approach outperforms the general multilingual model and performs close to its monolingual counterpart.
One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers (2026.acl-long)

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Challenge: Existing approaches to train multilingual large language models for many languages at once are limited due to limited model capacity, scarce high-quality data, and compute constraints.
Approach: They propose to use a universal tokenizer to improve language plasticity and adaptability to new languages by up to 20%.
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DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization (2025.acl-long)

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Challenge: Structured pruning reduces model size but often causes uneven degradation across domains, leading to biased performance.
Approach: They propose a method that dynamically adjusts the data distribution during training to restore balanced performance across heterogeneous and multi-tasking data.
Outcome: Experiments in monolingual and multilingual settings show that the proposed method surpasses similarly sized models in pruning and continued pretraining over perplexity, downstream tasks, and instruction tuning.

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