What Can Low Resource Languages Learn From Each Other?

• URL: http://arxiv.org/abs/2608.27753v1
• Date: Thu, 27 Aug 2026 22:32:35 GMT
• Title: What Can Low Resource Languages Learn From Each Other?
• Abstract summary: We propose a data-efficient framework that capitalizes on a cross-script "transfer effect"<n>Our approach first derives language-specific experts from a high-resource base model, then employs task arithmetic to fuse these experts into a unified, high-performance multilingual back- bone.
• Score: 4.131835597238715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the rapid advancement of Vision-Language Models (VLMs), their linguistic reach remains largely confined to high-resource languages, leaving the majority of the world's 7,000+ living languages on the wrong side of a growing digital divide. This disparity is especially pronounced in Optical Character Recognition (OCR), where low-resource scripts lack the massive datasets required for traditional scaling laws. We investigate OCR adaptation in extreme data-scarce regimes (<10K real and <250K synthetic images), demonstrating that conventional fine-tuning strategies often reach a performance ceiling. Our key finding reveals a structural inefficiency in language-specific adaptation: while higher layers of specialized models diverge to capture unique script nuances, the lower layers learn redundant, highly similar features. Motivated by this observation, we propose PSMC (Pre-train, Specialize, Merge, and Co-train), a data-efficient framework that capitalizes on a cross-script "transfer effect". Our approach first derives language-specific experts from a high-resource base model, then employs task arithmetic to fuse these experts into a unified, high-performance multilingual back- bone. Extensive evaluation across 10 Indian scripts (supporting 20+ languages) shows that PSMC achieves a ~2% average improvement in Word Recognition Rate (WRR) over individual specialist models without increasing parameter count. Our results indicate that joint training in the merged latent space facilitates a constructive knowledge transfer that benefits all constituent scripts, providing a scalable pathway for inclusive VLM development. Source code and datasets will be released post publication.

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)
• Multi-lingual Multi-institutional Electronic Health Record based Predictive Model [13.930489950153657]
  We show that text-based framework with lingual alignment effectively performs transfer learning via few-shot fine-tuning.<n>This is the first study to aggregate multilingual multinational ICU EHR datasets into one predictive model.
  arXiv  Detail & Related papers  (2026-03-11T07:21:27Z)
• Improving Language and Modality Transfer in Translation by Character-level Modeling [14.145120349133007]
  Current translation systems, despite being highly multilingual, cover only 5% of the world's languages.<n>We propose a character-based approach to improve adaptability to new languages and modalities.
  arXiv  Detail & Related papers  (2025-05-30T13:16:08Z)
• On Multilingual Encoder Language Model Compression for Low-Resource Languages [10.937645683754313]
  In this paper, we combine two-step knowledge distillation, structured pruning, truncation, and vocabulary trimming for extremely compressing multilingual encoder-only language models.<n>Our novel approach takes them to the extreme, reducing layer depth, feed-forward hidden size, and intermediate layer embedding size to create significantly smaller monolingual models.<n>We achieve compression rates of up to 92% while maintaining competitive performance, with average drops of 2-10% for moderate compression and 8-13% at maximum compression.
  arXiv  Detail & Related papers  (2025-05-22T17:35:39Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Building an Efficient Multilingual Non-Profit IR System for the Islamic Domain Leveraging Multiprocessing Design in Rust [0.0]
  This work focuses on the development of a multilingual non-profit IR system for the Islamic domain. By employing methods like continued pre-training for domain adaptation and language reduction to decrease model size, a lightweight multilingual retrieval model was prepared.
  arXiv  Detail & Related papers  (2024-11-09T11:37:18Z)
• Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment [31.885608173448368]
  Pre-trained vision and language models such as CLIP have witnessed remarkable success in connecting images and texts with a primary focus on English texts. disparities in performance among different languages have been observed due to uneven resource availability. We propose a new parameter-efficient cross-lingual transfer learning framework that utilizes a translation-based alignment method to mitigate multilingual disparities.
  arXiv  Detail & Related papers  (2023-05-02T14:09:02Z)
• Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation [50.54059385277964]
  We present a novel data augmentation paradigm termed Continuous Semantic Augmentation (CsaNMT) CsaNMT augments each training instance with an adjacency region that could cover adequate variants of literal expression under the same meaning.
  arXiv  Detail & Related papers  (2022-04-14T08:16:28Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval [51.60862829942932]
  We present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. For sentence-level CLIR, we demonstrate that state-of-the-art performance can be achieved. However, the peak performance is not met using the general-purpose multilingual text encoders off-the-shelf', but rather relying on their variants that have been further specialized for sentence understanding tasks.
  arXiv  Detail & Related papers  (2021-01-21T00:15:38Z)
• 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.