Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models (2025.coling-main)
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| Challenge: | Existing methods to augment vocabularies ignore the disparities between model representation and frequency distributions. |
| Approach: | They propose an Entropy-Consistency Word Selection method which integrates semantic and frequency metrics for vocabulary augmentation. |
| Outcome: | The proposed method improves performance for low-resource languages compared to high-resourced ones . it integrates semantic and frequency metrics for vocabulary augmentation . |
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Entropy-guided Vocabulary Augmentation of Multilingual Language Models for Low-resource Tasks (2023.findings-acl)
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| Challenge: | Multilingual language models (MLLMs) support low-resource languages (LRLs) but LRL words are under-represented in wordpiece/subword vocabularies, leading to low task accuracy . |
| Approach: | They propose an entropy-based vocabulary augmented language model to detect LRL words with undesirable wordpiece segmentations. |
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Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)
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ChangSu Choi, Yongbin Jeong, Seoyoon Park, Inho Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
| Challenge: | Large language models (LLMs) use pretraining to predict the subsequent word, but less-resourced languages are being overlooked. |
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Improving Low-Resource Languages in Pre-Trained Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Pre-trained multilingual language models are the foundation of many NLP approaches, but are often not well-supported by these models due to small available monolingual corpora. |
| Approach: | They propose an unsupervised approach to improve cross-lingual representations of low-resource languages by bootstrapping word translation pairs from monolingual corpora and using them to improve language alignment. |
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Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)
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| Challenge: | Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data. |
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Adapters for Enhanced Modeling of Multilingual Knowledge and Text (2022.findings-emnlp)
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| Challenge: | Large language models learn facts from text corpora, but knowledge graphs contain facts in an explicit triple format, restricting their research and application. |
| Approach: | They propose to enhance multilingual language models with knowledge from multilingual knowledge graphs . they propose to use cross-lingual entity alignment and facts from MLKGs to improve performance . |
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VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks . |
| Approach: | They propose a low-resource multilingual large language model that expands the vocabulary for multilingual support. |
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Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. |
| Approach: | They propose a simple but effective approach to extend multilingual BERT to any new language and show an increase in F1 on M-BERT and new languages. |
| Outcome: | The proposed approach improves on languages already in M-BERT and out of it on other languages. |
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)
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| Challenge: | Several strategies have been proposed to enhance performance in low-resource scenarios. |
| Approach: | They propose to use 5 low-resource strategies for dependency parsing for multiple languages . they use ensembled approach on 7 UD low-rsource languages based on their results . |
| Outcome: | The proposed approach improves on a low-resource language Sanskrit. |
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)
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| Challenge: | Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial . |
| Approach: | This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). |
| Outcome: | This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models. |
Targeted Multilingual Adaptation for Low-resource Language Families (2024.findings-emnlp)
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| Challenge: | Massively multilingual models are known to have limited utility in any one language, and to perform poorly on low-resource languages. |
| Approach: | They propose to adapt a pre-trained multilingual model to a language family and evaluate its performance on two downstream tasks and 11 evaluation languages. |
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