The paper introduces SinLlama, the first decoder‑based open‑source large language model with explicit support for Sinhala. By extending Llama‑3‑8B, adding Sinhala‑specific tokenizer vocabulary, and performing continual pre‑training on a cleaned 10‑million‑token Sinhala corpus, the authors created a model that surpasses both the base and instruction‑fine‑tuned variants of Llama‑3‑8B on three text classification tasks. This work addresses the underrepresentation of low‑resource languages in open‑source LLMs.
By H. W. K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Surangika Ranathunga, Rishemjit Kaur
The paper introduces a compact BART-based sequence‑to‑sequence model for low‑latency spell correction of Japanese music search queries, addressing challenges posed by four writing scripts. It employs a script‑aware synthetic misspelling pipeline that blends keyboard‑layout models, phonetic confusion priors, consonant alternations, and kana case errors, and normalizes mixed‑script titles to a single canonical script to reduce hallucinations. Experiments show the model achieves 41.09% exact‑match accuracy and 11.62% CER, outperforming edit‑distance baselines while keeping inference latency under 4 ms on a single GPU.
By Anshul Garg, Pavni Tandon, Karan Bhukar, Tanmay Khandelwal, Ujjal Kumar Dutta
The paper presents an end‑to‑end sequence‑to‑sequence approach for correcting Tamil spelling and grammar errors, leveraging progressively fine‑tuned transformer models (mT5‑small and mBART‑50). Using a synthetic corpus of 657,720 noisy‑clean sentence pairs across ten error categories, the authors introduce a four‑stage training schedule that targets surface noise, contextual grammar, single‑site sandhi, and multi‑site cross‑word sandhi. The best model, mBART‑50 v5, achieves 69.3% exact‑match accuracy on a balanced diagnostic set, with notable gains in sandhi (87.5%) and subject‑verb agreement (43.5%) accuracy, while also revealing a precision‑recall trade‑off for sandhi corrections.
By Karthikeyan A, Jaya Nirmala S, Sangeetha Sivanesan, Indhu R, Pranav Kumar, Bharat Jude Johnson, Vishnu Ram
arXiv:2610.15056v1 Announce Type: cross
Abstract: Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improv...
By Shiran Liu, Gaohui Xu, Sergio Burdisso, Thibault Ba\~neras-Roux, Shashi Kumar, Petr Motlicek
arXiv:2605. 20712v2 Announce Type: replace-cross Abstract: Automatic speech recognition replaces typing only when correction costs less than manual entry - a threshold determined by error types, not counts: fixing a misrecognized domain term costs far more than inserting a comma.
By Kavya Manohar, Arghya Bhattacharya, Kush Juvekar, Kumarmanas Nethil
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y
arXiv:2609.15559v1 Announce Type: cross
Abstract: Grammatical error correction (GEC) evaluation has traditionally relied on reference or edit overlap, which can penalize valid rewrites that differ fr...
By Hayeong Ryu, Sunhee Jo, Seunguk Yu, YoungBin Kim
arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.
By Wei Tian, Yuhao Zhou, Man Lan
arXiv:2609.16340v1 Announce Type: cross
Abstract: Machine translation systems are periodically upgraded to stronger models, but the available preference signal is human post-edits of an older system'...
By Rohit Dhaipule, Sukhdeep Singh Kharbanda, Prasanth Bathala, Pradyumna Lanka, Anubhav Shrimal
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
The paper presents a prompt-based method for minimal-edit grammatical error correction (GEC) that reduces overcorrection in large language models (LLMs). It introduces taxonomy-based instructions, batch prompting to regularize overcorrection, and LLM-assisted prompt optimization, achieving an $F_{0.5}$ score of 78.32 on BEA-2019 with Gemini 3.1-Pro. This approach narrows the performance gap to fine-tuned models while avoiding their infrastructure demands.
By Kateryna Karpo, Artem Chernodub
The paper introduces a black‑box, inference‑time diagnostic for low‑resource Automatic Post‑Editing (APE) that distinguishes whether poor performance is due to insufficient training data or inconsistent training signals. By varying an edit‑distance penalty and analyzing the resulting TER‑vs‑λ curve and confidence‑based constraint ordering, the authors identify two failure modes—Binary Collapse and Confident Miscalibration—across multiple language pairs. The diagnostic also suggests practical next steps, such as applying a static constraint for immediate accuracy gains, and the authors release new English‑Sinhala and English‑Tamil APE datasets with accompanying code.
By Isuru Wijesiri, Nisansa de Silva, Kavindu Warnakulasuriya, Aloka Fernando, Surangika Ranathunga