Modular Monolingual Adaptation using Pretrained Language Models

• URL: http://arxiv.org/abs/2606.06738v1
• Date: Thu, 04 Jun 2026 21:51:50 GMT
• Title: Modular Monolingual Adaptation using Pretrained Language Models
• Abstract summary: Building monolingual language models (LMs) for low-resource languages typically relies on adapting pretrained language models (PLMs) by finetuning the whole model on the target language.<n>In this work, we hypothesize that full model tuning is often unnecessary and propose a more modular approach. Specifically, we replace the tokens, freeze the corresponding embeddings, and tune the rest of the model.
• Score: 1.7188280334580195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building monolingual language models (LMs) for low-resource languages typically relies on adapting pretrained language models (PLMs) by finetuning the whole model on the target language. This approach is widely favored over training from scratch, as it enables effective knowledge transfer. Additionally, prior work has shown that using a language-specific tokenizer can enhance the adaptability. In this work, we hypothesize that full model tuning is often unnecessary and propose a more modular approach. Specifically, we replace the tokens, freeze the corresponding embeddings, and tune the rest of the model. We use Scottish Gaelic, Irish, and Quechua for our experiments, with Quechua being a very low-resource language (8.5k training instances). Evaluation on natural language understanding (NLU) tasks -- mask filling, NER, and POS -- shows that our proposed approach improves performance when adapting models to low-resource languages. Additionally, we provide a comprehensive analysis of the effectiveness of training strategies, the choice of pretrained embeddings, and models.

Related papers

• Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation [50.731838216268706]
  We study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus.<n>We find that continual multilingual pre-training leads to diffused, language-agnostic routing in early and middle layers.<n>We propose a parameter-efficient adaptation strategy that updates language-specific and shared experts in the final MoE layers.
  arXiv  Detail & Related papers  (2026-05-28T10:12:41Z)
• Targeted Multilingual Adaptation for Low-resource Language Families [17.212424929235624]
  We study best practices for adapting a pre-trained model to a language family. Our adapted models significantly outperform mono- and multilingual baselines. Low-resource languages can be aggressively up-sampled during training at little detriment to performance in high-resource languages.
  arXiv  Detail & Related papers  (2024-05-20T23:38:06Z)
• LlamaTurk: Adapting Open-Source Generative Large Language Models for Low-Resource Language [2.9914612342004503]
  This study explores an alternative solution by adapting large language models, primarily trained on English, to low-resource languages. We assess various strategies, including continual training, instruction fine-tuning, task-specific fine-tuning, and vocabulary extension. The results show that continual training improves language comprehension, as reflected in perplexity scores, and task-specific tuning generally enhances performance of downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T13:41:59Z)
• Efficiently Adapting Pretrained Language Models To New Languages [9.33333013114014]
  Recent large language models (LLM) exhibit sub-optimal performance on low-resource languages. We study how to efficiently adapt any existing pretrained LLM to a new language without running into these issues.
  arXiv  Detail & Related papers  (2023-11-09T20:59:08Z)
• Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation [19.624330093598996]
  Training monolingual language models for low and mid-resource languages is made challenging by limited and often inadequate pretraining data. By generalizing over a word translation dictionary encompassing both the source and target languages, we map tokens from the target tokenizer to semantically similar tokens from the source language tokenizer. We conduct experiments to convert high-resource models to mid- and low-resource languages, namely Dutch and Frisian.
  arXiv  Detail & Related papers  (2023-10-05T11:45:29Z)
• Improving Language Plasticity via Pretraining with Active Forgetting [63.36484652568976]
  We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Experiments with RoBERTa show that models pretrained with our forgetting mechanism demonstrate faster convergence during language adaptation.
  arXiv  Detail & Related papers  (2023-07-03T17:12:44Z)
• Unsupervised Improvement of Factual Knowledge in Language Models [4.5788796239850225]
  Masked language modeling plays a key role in pretraining large language models. We propose an approach for influencing pretraining in a way that can improve language model performance on a variety of knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-04T07:37:06Z)
• Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation [129.99918589405675]
  Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Multilingual fine-tuning improves performance on low-resource languages but requires modifying the entire model and can be prohibitively expensive. We propose training language-family adapters on top of mBART-50 to facilitate cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-09-30T05:02:42Z)
• Lifting the Curse of Multilinguality by Pre-training Modular Transformers [72.46919537293068]
  multilingual pre-trained models suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We introduce language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. Our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
  arXiv  Detail & Related papers  (2022-05-12T17:59:56Z)
• Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation [127.81351683335143]
  Cross-lingual pretraining requires models to align the lexical- and high-level representations of the two languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings.
  arXiv  Detail & Related papers  (2021-03-18T21:17:58Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.