Low-Resource Language Processing

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Benchmarking Parameter-Efficient Fine-Tuning of Large Language Models for Low-Resource Tajik Text Generation with the Tajik Web Corpus
May 5, 2026Mullosharaf K. ArabovLLM Fine-Tuning Low-Resource Language Processing
Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia
Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
May 4, 2026Kevin Guan, Happy Buzaaba, Christiane FellbaumLow-Resource Language Processing Dependency Parsing
Princeton University
SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
May 4, 2026Nedjma Ousidhoum, Junho Myung, Carla Perez-Almendros +27Multilingual Language Model Evaluation LLM Evaluation
Cardiff University · KAIST · MBZUAI +13
Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation
May 4, 2026Jiaxu He, Chao Wang, Jie Lian +4TTS Synthesis Low-Resource TTS Synthesis
Xingchen AGI Lab, China Telecom Artificial Intelligence Technology Co. Ltd · Qinghai Normal University, Xining, China · University of Electronic Science and Technology of China,Chengdu,China +1
Automatic Reflection Level Classification in Hungarian Student Essays
May 4, 2026Zsolt Csibi, Mónika Sándor, Mónika Serfőző +2Automated Essay Scoring Low-Resource Language Processing
Department of Artificial Intelligence, Faculty of Informatics, Eötvös Lor´and University, Budapest, Hungary · Department of Education, Faculty of Primary and Pre-school Education, Eötvös Lor´and University, Budapest, Hungary
Cross-Lingual Learning within Arabic Script for Low-Resource HTR
May 3, 2026Sana Al-azzawi, Elisa Barney, Marcus LiwickiOnline Handwriting Recognition Low-Resource Language Processing
Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Luleå, 97187, Sweden
SiNFluD: Creating and Evaluating Figurative Language Dataset for Sindhi
May 2, 2026Wazir Ali, Adeeb Noor, Saifullah TumraniFigurative Language Understanding Text Classification
Department of Data Science, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan. · SoloGenAI Pvt. Ltd. · Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. +1
Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach
May 2, 2026Ahmed Alfey Sani, Kazi Akib Zaoad, Shefayat E Shams Adib +2Class-Imbalanced Learning Synthetic Data Augmentation
Islamic University of Technology Gazipur, Bangladesh
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues
Apr 30, 2026Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan +13Multilingual Language Model Evaluation Arabic NLP
Mohamed bin Zayed University of Artificial Intelligence · IBM Research AI · American University in the Emirates
Linguistically Informed Multimodal Fusion for Vietnamese Scene-Text Image Captioning: Dataset, Graph Framework, and Phonological Attention
Apr 30, 2026Nhi Ngoc-Yen Nguyen, Anh-Duc Nguyen, Nghia Hieu Nguyen +2Image Captioning Low-Resource Language Processing
Faculty of Information Science and Engineering · University of Information Technology · Vietnam National University, Ho Chi Minh, Viet Nam
Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments
Apr 30, 2026Emilia Milano, Alistair Plum, Yves Scherrer +1Low-Resource Language Processing
University of Luxembourg, Esch-sur-Alzette, Luxembourg · University of Oslo, Oslo, Norway
Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation
Apr 29, 2026Akhil Rajeev P, Annarao KulkarniNamed Entity Recognition Synthetic Data Generation
Centre for Development of Advanced Computing (C-DAC), Bangalore
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
Apr 28, 2026Nayeon Lee, Jiwoo Song, Byeongcheol KangRetrieval-Augmented Generation Adaptive RAG
Naver · Samsung Research
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards
Apr 28, 2026Yiming Ni, Zhi-Qi Cheng, Jiayu Li +1Sign Language Recognition Low-Resource Language Processing
Tacoma School of Engineering & Technology, University of Washington
Language corpora for the Dutch medical domain
Apr 28, 2026B. van EsHealthcare Low-Resource Language Processing
Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht, The Netherlands. · R&D, B-lab, Castricum, The Netherlands.
IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning
Apr 27, 2026Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand +8Cross-Lingual Reasoning in Language Models RL for Language Model Reasoning
1Singapore Institute of Technology, Singapore · 2IIIT Delhi, New Delhi, India · University of California, San Diego, USA +2
Resource-Lean Lexicon Induction for German Dialects
Apr 26, 2026Robert Litschko, Barbara Plank, Diego FrassinelliLow-Resource Language Processing
MaiNLP, Center for Information and Language Processing, LMU Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany
Neural Grammatical Error Correction for Romanian
Apr 26, 2026Teodor-Mihai Cotet, Stefan Ruseti, Mihai DascaluGrammatical Error Correction Low-Resource Language Processing
Computer Science Department University Politehnica of Bucharest Bucharest, Romania
XITE: Cross-lingual Interpolation for Transfer using Embeddings
Apr 26, 2026Barah Fazili, Preethi JyothiMultilingual Language Models Data Augmentation
Indian Institute of Technology Bombay, India
Zero-Shot Morphological Discovery in Low-Resource Bantu Languages via Cross-Lingual Transfer and Unsupervised Clustering
Apr 24, 2026Hillary Mutisya, John MuganeLow-Resource Language Processing
Thiomi NLP · Harvard University
Multilinguality at the Edge: Developing Language Models for the Global South
Apr 23, 2026Lester James V. Miranda, Songbo Hu, Roi Reichart +1Multilingual Language Models On-Device Language Model Inference
Language Technology Lab, University of Cambridge, United Kingdom · Technion – Israel Institute of Technology, Israel
CARE: Counselor-Aligned Response Engine for Online Mental-Health Support
Apr 23, 2026Hagai Astrin, Ayal Swaid, Avi Segal +1Mental Health Counseling Emotional Support Conversation
Ben-Gurion University · School of Informatics, University of Edinburgh Edinburgh, UK
Machine learning and emoji prediction: How much accuracy can MARBERT achieve?
Apr 22, 2026Mohammed Q. Shormani, Ibrahim Abdulmalik Hassan Muneef Y. AlshawshArabic NLP Low-Resource Language Processing
Ibb University, Yemen
AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Models
Apr 22, 2026Tadesse Destaw Belay, Shahriar Kabir Nahin, Israel Abebe Azime +6Multilingual Language Models Intelligent Tutoring Systems
Instituto Politécnico Nacional, Mexico · University of South Florida, FL, USA · Saarland University, Germany +2
RespondeoQA: a Benchmark for Bilingual Latin-English Question Answering
Apr 22, 2026Marisa Hudspeth, Patrick J. Burns, Brendan O'ConnorMultilingual Language Model Evaluation Multilingual QA
Manning College of Information & Computer Sciences, University of Massachusetts Amherst · Institute for the Study of the Ancient World, New York University
Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection
Apr 22, 2026Yassine Turki, Vinko Sabolčec, Bettina Messmer +1Language Model Pretraining Multilingual Language Models
Machine Learning Optimization Lab Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne, Switzerland
Effects of Cross-lingual Evidence in Multilingual Medical Question Answering
Apr 22, 2026Anar Yeginbergen, Maite Oronoz, Rodrigo AgerriKnowledge Augmentation for Language Models Healthcare
HiTZ Center - Ixa, University of the Basque Country UPV/EHU
RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romanian
Apr 21, 2026Mircea Timpuriu, Mihaela-Claudia Cercel, Dumitru-Clementin CercelLegal NLP Grammatical Error Correction
National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania · Paris 1 Panthéon-Sorbonne University, Paris, France · University of Bucharest, Bucharest, Romania
A Bolu: A Structured Dataset for the Computational Analysis of Sardinian Improvisational Poetry
Apr 21, 2026Silvio Calderaro, Johanna MontiLow-Resource Language Processing
Università di Pisa, Italia · Università di Napoli L’Orientale, Italia
Bangla Key2Text: Text Generation from Keywords for a Low Resource Language
Apr 21, 2026Tonmoy Talukder, G M ShahariarLow-Resource Language Processing Text Generation
1Ahsanullah University of Science and Technology · University of California - Riverside

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Benchmarking Parameter-Efficient Fine-Tuning of Large Language Models for Low-Resource Tajik Text Generation with the Tajik Web Corpus

Mullosharaf K. Arabov

Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan, Russia

LLM Fine-Tuning Low-Resource Language Processing Text Generation Parameter-Efficient Fine-Tuning

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This paper is devoted to the adaptation of generative large language models for the Tajik language, a low-resource language with Cyrillic script. To overcome the shortage of digital text resources, the author created and publicly released the Tajik Web Corpus, the largest open-access corpus of Tajik, comprising 319,298 documents (~1.11 billion characters). On a subsample of 10,000 documents, 17 configurations were benchmarked, covering autoregressive, encoder-decoder, and encoder-only models with three fine-tuning strategies: full fine-tuning, LoRA, and QLoRA (ranks 8 and 16). Quality was assessed via perplexity and cross-entropy loss; peak GPU memory and training time were also recorded. Best results were achieved by Mistral 7B with QLoRA (r=16): mean perplexity 5.03, standard deviation 0.03. Increasing rank from 8 to 16 gave statistically insignificant improvement while raising memory consumption. For small GPT-2 family models, full fine-tuning yielded lower perplexity (3.48 for GPT-2 Medium) than LoRA (7.60-8.42), but induced catastrophic forgetting. The encoder-only XLM-RoBERTa showed the worst results (perplexity 59.3). The novelty lies in creating the largest verified Tajik corpus and the first systematic analysis of PEFT effectiveness for Tajik text generation. Practical value lies in recommendations for architecture and fine-tuning strategy selection, optimizing computational costs without substantial quality loss.

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