Hugging Face Trending Papers

TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation

TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a suite of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can discard up to 96% of training tokens while preserving quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this curated mixture outperform larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.

arXiv Computation and Language

Aug 27

TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation

TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a family of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can remove up to 96% of training tokens without harming quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this mixture outperform much larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.

By Milan Gritta, Patrik Lambert, Jihye Back, Amril Nazir

llms nlp fine-tuning

arXiv Machine Learning

Aug 11

Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation

arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.

By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli

llms rag nlp fine-tuning

arXiv AI

Oct 15

Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs

arXiv:2610.18818v2 Announce Type: cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource langu...

By Divyanshu Bathla, Parag Singla, Jitin Singla

llms nlp fine-tuning

arXiv Computation and Language

Sep 16

ReMova: Fine-tuning LLMs for English to Belarusian translation

The paper introduces ReMova, a pipeline for cleaning Belarusian data and fine‑tuning large language models (LLMs) for English‑to‑Belarusian translation. It uses a correction tool to handle the two orthographies of Belarusian, remove noise, filter out interference from other languages, and correct common misspellings found online. Ablation experiments on unfiltered data show that filtering benefits all fine‑tuned models, with LLM‑based models gaining about twice as much as a dedicated encoder‑decoder MT system, highlighting data quality as a key bottleneck for Belarusian MT.

By Mikita Pilinka, Aliaksandr Kliuje\u{u}, David Samuel, Yves Scherrer

llms nlp fine-tuning benchmarks data-engineering

arXiv Computation and Language

Sep 25

Benchmarking Arabic--Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLMs under Rich Morphology and Low Lexical Overlap

The study evaluates Arabic–Russian machine translation by comparing seven fine‑tuned neural machine translation (NMT) models with four few‑shot large language models (LLMs) on a new 15.47 million‑pair corpus split into 20k/5k/5k. Fine‑tuned NLLB‑1.3B achieves the best performance (BLEU 16.3, COMET 0.738), while the best few‑shot LLM, Aya‑Expanse 8B, scores only BLEU 1.7 on 500 sentences. Error analysis shows that low lexical overlap between Arabic and Russian is the main source of failures, and statistical tests confirm significant performance gaps between most models.

By Mullosharaf K. Arabov

llms nlp fine-tuning benchmarks

arXiv AI

Oct 9

Adapting English Quality Classifiers for Multilingual LLM Pretraining Data Selection

The paper proposes a multilingual adaptation of an English quality classifier to select high‑quality training data for large language model pretraining across more than 100 languages. The method trains a small multi‑layer perceptron on Transformer embeddings, using machine‑translated text scored by the English classifier as labels. Experiments on 1B, 3B, and 8B parameter models show that the adapted classifier preserves benchmark performance and regional knowledge, and it generalizes to unseen languages by learning the original scoring criteria.

By Vinko Sabol\v{c}ec, Bettina Messmer, Yassine Turki, Martin Jaggi

llms rag benchmarks

arXiv Computation and Language

Sep 4

Last Translation Benchmark

The paper introduces the Last Translation Benchmark (LTB), a live dataset of human-authored and peer‑reviewed examples—including texts, images, audio, and videos—that are designed to break current state‑of‑the‑art machine translation models. Each example is accompanied by handcrafted verification rules that specify concrete failure cases, providing a reliable and actionable evaluation method. The benchmark aims to overcome the limitations of existing automatic metrics and gold human evaluations, which often lack reproducibility, objectivity, and scalability.

By Vil\'em Zouhar, Niyati Bafna, Mukund Choudhary, Maike Z\"ufle, Sara Rajaee, Pinzhen Chen, Jannis Vamvas, Sara Papi, Ona de Gibert, Bhavitvya Malik, Eliya Habba, Orfeas Menis Mastromichalakis, Patr\'icia Schmidtov\'a, Michelle Wastl, Sheriff Issaka, Leshem Choshen, Stella Biderman, Antonis Anastasopoulos, Jan Niehues, Rico Sennrich, Mrinmaya Sachan, Ond\v{r}ej Bojar, Kenton Murray, J\"org Tiedemann, Alham Fikri Aji, Philipp Koehn, Christof Monz, Alexandra Birch, Sowmya Vajjala, Chalamalasetti Kranti, Cristina Espa\~na-Bonet, Nobin Sarwar, David Kacz\'er, Shunta Asano, Malik Marmonier, Daban Q. Jaff, Vaisakhi Mishra, Hend Al- Khalifa, Gabriele Sarti, Sourajit Saha, Nils Rehlinger, Juan Daniel Cuervo Villa, Jonathan Tonglet, Saugata Purkayastha, Dominik Mach\'a\v{c}ek, Jagannathan Ramanujam, Heejin Do, Zuzana Nadova, Fred Philippy, Fabian Retkowski, Maria Lymperaiou, Silvia Casola, Hanna Yukhymenko, Shubhashis Roy Dipta, Sangwon Ryu, Andr\'es Jerez, Ron Keinan, Shuaib Shuaib Yusuf, Avantica Vempati, Maria Carmen Staiano, Sukannya Purkayastha, Adrian Cosma, Vitalii Babenko, Erivan Inan, Aviral Nigam, Wafa Aissa, Fatima Haouari, Venkata Prasanth Kumar Gummadi, Mehdi Jafarzadeh, Valentin Scourneau, Lukas Edman, Kaiser Sun, Shaomu Tan, Mohammad Sadegh Gholizadeh, Johannes-Rudolf David, Dipankar Srirag, Javier Garc\'ia Gilabert, Ruta Binkyte, Manar Ali, Ana-Maria Bucur, Sabry E. Farrag, Youssef Saber, Yihong Liu, Jean Maillard, Cojocaru Nicoleta, Xiaochuang Yuan, Sina Ahmadi, Philipp Mondorf, Kaustubh Dhole, Roman Wixinger, Shenbin Qian, Manuel Tuor, Sergey Troshin, Jonathan Yahav, Fida Mohammad Thoker, Amir Arsalan Rezapour, Lance Calvin Lim Gamboa, Manon Reusens, K\"atriin Kukk, Koel Dutta Chowdhury, Giuseppe Gallipoli, Christian Hoang, Shaswati Saha, Seth Aycock, Jan Koco\'n, Bo Chen, Linh Vu, Vatsal Venkatkrishna, Arafat Ahsan, Luan Thanh Nguyen, Hassan Soliman, Daryna Dementieva, Theresia Veronika Rampisela, Ngoc Quynh Tram Do, Marius Huber, Kazuki Egashira, Azmine Toushik Wasi, Vladislav Poritski, Mike Zhang, Deep Shah, Paul Gavrikov, Luis Frentzen Salim, David Africa, R. Damanhuri, Bello Umar Bello, Anumit Garg, Gengyu Rao, Pawan Sasanka Ammanamanchi, Kamile Dementaviciute, Andrianos Michail, L D M S Sai Teja, Dawei Zhu, Yi Fan, Wei Liu, Farhan Farsi, Elias Herranen, Sankalan Pal Chowdhury, Karen Sanchez, Farzad Shami, Ashok Urlana, Zimu Wang, Tomasz Limisiewicz, Priyaranjan Pattnayak, Marii Ojastu, Hongbin Na, Emilian Radoi, Chenyi Zhao, Carlos Hinojosa, Andrea Gregor de Varda, Zaid Alyafeai, Reem Alzahrani, Nehal Kathrotia, Alex Fl\"uckiger, Ulysses Sekai Tully Carr, Jimson Paulo Layacan, Guy Kaplan, Ritwik Tiwari, Rishit Dagli, Oksana Volchek, Isaac R Caswell, Bowen Yi, Blanka K\"ov\'er, Amir Hossein Yari, Aicha Chorana, Zhengxiang Wang, Selja Ker\"anen, Samuel Simko, Joy Olusanya, Jenny Chim, Enzo Doyen, Vivek Harsha Lakkamaneni, Sophia Conrad, Pouya Sadeghi, Panayiotis Panayiotou, Luis Lara, Jannatul Nayem, Eran Yahav, Debanshu Das, Antonia Karamolegkou, Anmol Goel, Aishik Mandal, Tommaso Cerruti, Raoyuan Zhao, Mykola Haltiuk, Thura Aung, Naser Almousa, Amir Hossein Kargaran, Rachel Bawden, Qiaoyuan Zheng, Mateusz Lango, Beni Egressy, Fidel Rodr\'iguez Vel\'asquez, Natchapon Jongwiriyanurak, Minh Ngoc Do, Marco Gaido, Lena Libon, Dzmitry Kuzmin, Badal Nyalang, Antoine Taroni, Andrei Niculae, Abdulaziz Nura Kani, Rushikesh Zawar, Marek \v{S}uppa, Beatrice Savoldi, Andreas Simons, Rayyan Merchant, Ilai Yaron Levy, Francesco Pinto, Ziyi Yang, Yolanda Xavier, Samuel Frontull, Muhammad Ravi Shulthan Habibi, Kenneth Enevoldsen, Harris Abdul Majid, Francesca Padovani, Tim Graf, Tatiana Bielakova, Sharifa Djurabaeva, Shaoxiong Ji, Raia Abu Ahmad, Pavel Stepachev, Jirui Qi, Ayush Sunil Munot, Alireza Pakniat, Ayla Rigouts Terryn, Yuxing Lu, Yurii Paniv, Xiyan Fu, Tosin Adewumi, Sunisth Kumar, St\'ephane J. P. S. Thunus, Shree Harsha Bokkahalli Satish, Shayan Bali, Prakhar Gupta, Papa Abdou Karim Karou Diallo, Matija Akrap, Marko Culjak, Krist\'yna Onderkov\'a, Joseph Attieh, Esrael Teferi Tensay, Elisabeth Fittschen, Beno\^it Sagot, Jingwei Ni, Yu Fan

nlp benchmarks

arXiv Computation and Language

2d ago

Dataset Collection and Fine-Tuning of NLLB for German--English Translation

arXiv:2611.07797v1 Announce Type: new Abstract: This paper describes the curation of a high-quality parallel corpus from OPUS Books and the fine-tuning of Meta's NLLB-200 models for German--English t...

By SK Deeraj, Yash Ingle, Pruthwik Mishra

fine-tuning

arXiv Computation and Language

Sep 10

MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages

arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...

By Maximilian Idahl, J\"org Tiedemann, Sampo Pyysalo, David Salinas, Tomasz Galica, Shenbin Qian, Tudor Nicolae Mateiu, Zihao Li, Anna Lokrantz, Fedor Vitiugin, Andr\'e F. T. Martins, Jenna Kanerva, Filip Ginter, Matthias Lindemann, Tim Isbister, Birger Moell, Jonas Lindh, Jan Haji\v{c}, Jenia Jitsev, Andrey Kutuzov, Stephan Oepen, Gema Ram\'irez-S\'anchez

llms benchmarks

arXiv AI

Aug 12

Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

arXiv:2608. 10812v1 Announce Type: cross Abstract: We study reference-free post-training for multilingual machine translation with open large language models.

By Chris Han, Pengzhi Gao, Pei Fu, Jian Luan

llms nlp reinforcement-learning fine-tuning efficiency

arXiv Computation and Language

Sep 15

North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)

arXiv:2609.13916v1 Announce Type: new Abstract: We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same fo...

By Tom Kocmi, Alexandre B\'erard, Phil Blunsom, Samuel Cahyawijaya, Shaun Cassini, Nicholas Frosst, Ona de Gibert, Aidan Gomez, Nithya Govindarajan, Shun Kiyono, Olivia Lasche, Lawrence Rogers, Kelly Marchisio, Nikita Moghe, Yash More, Camila Moran-Hidalgo, Yiyang Nan, Michael Sachs, Trisha Starostina, Daan van Stigt, Spencer Rarrick, Sebastian Vincent, Ivan Zhang

llms agents nlp reinforcement-learning fine-tuning efficiency

arXiv Computation and Language

Sep 25

EnSiTa - A Trilingual Multi-Domain Parallel Dataset and Benchmark for Domain-Specific Machine Translation

EnSiTa is a trilingual multi‑domain parallel dataset and benchmark for English, Sinhala, and Tamil. It contains human post‑edited training data across seven domains and professionally translated test sets for those domains plus an additional one, all produced through a multi‑year, rigorously quality‑controlled process. The authors use EnSiTa to conduct a comprehensive study of domain‑specific machine translation across six language directions, comparing from‑scratch Transformers, pre‑trained models, and decoder‑only LLMs under various training‑data sizes, model scales, and domain settings.

By Surangika Ranathunga, Nisansa de Silva, Aloka Fernando, Kavindu Warnakulasuriya, Isuru Wijesiri, Menan Velayuthan, Charitha Rathnayaka, Thivaharan Varatharajan, Sajeevi Silva, Piumi Kandanaarachchi, Uthayasanker Thayasivam

llms nlp fine-tuning benchmarks

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