MoganColBERT-TR is a Turkish multi‑vector retrieval model that projects token embeddings from 768 to 128 dimensions and uses MaxSim late interaction for scoring. It builds on the previously trained MoganBERT‑TR encoder, adapting it to the ColBERT objective with a single‑epoch distillation phase using cross‑encoder teacher scores. Evaluated on five Turkish BEIR datasets in a zero‑shot setting, it achieves an overall score of 37.36, outperforming larger models on most datasets.
By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay
arXiv:2512.23065v4 Announce Type: replace
Abstract: The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models...
By Melik\c{s}ah T\"urker, A. Ebrar K{\i}z{\i}lo\u{g}lu, Onur G\"ung\"or, Susan \"Usk\"udarl{\i}
arXiv:2606. 18717v1 Announce Type: cross Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text.
By Tolga \c{S}akar
arXiv:2610.22529v1 Announce Type: new
Abstract: Autoregressive foundation language models predominantly target high-resource Indo-European languages, imposing computational and morphological penaltie...
By Wesley Kambale, Roland Sankara
arXiv:2607.09885v3 Announce Type: replace
Abstract: We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundatio...
By Tianjiao Li, Lusheng Zhang, Shien He, Xiaojing Liu, Tianxing Yan, Mengran Yu, Ziang Cui, Kai Zhao, Xipeng Wang, Yang Liu, Yuxin Li
The paper introduces ufakzeka-1, a 151‑million‑parameter Turkish language model trained from scratch on 13.5 B tokens. It details the tokenizer, a three‑stage pretraining schedule, post‑training data augmentation, and a comprehensive evaluation suite that includes release gates, a rule‑checked conversation sweep, and hand tests—all with prompts excluded from training data. The authors report three key findings about safety gate performance, training‑seed variance, and data‑round effects, and they release the model weights, data recipe, evaluation code, and spend ledger under Apache‑2.0.
By Sait Furkan Teke (ufak AI)
Neurograf v1 is an experimental end‑to‑end Romanian‑first language‑model pipeline that includes a 635 GB Romanian corpus (RoSfera), an 80k byte‑level BPE tokenizer, and five decoder‑only models ranging from 160 M to 6 B parameters trained on 131 B tokens. The pipeline also produces three instruction‑tuned assistants and two contrastively trained embedding models, all trained on ELI‑NP/IFIN‑HH infrastructure. Evaluation shows consistent scaling of validation loss and improved Romanian factual and cultural knowledge with larger models, achieving 69.89 % accuracy on the 1,000‑question WWTBM benchmark for the 6 B model.
"whyItMatters":"The study demonstrates that it is feasible to train Romanian‑first language models from scratch, highlighting practical challenges such as diacritic preservation and tokenization efficiency while providing a unified training pipeline for base, instruction‑following, and embedding models."
By Ionut Adrian Rotaru
Manacá-1B is a 1.72‑billion‑parameter, open decoder‑only language model trained from scratch for Brazilian Portuguese, released with a fully containerized, reproducible training pipeline and complete logs. The authors evaluate it against nine open baselines on four Portuguese benchmarks, reporting standard errors and paired significance tests, and find that Manacá-1B outperforms smaller models on LAMBADA‑PT while remaining competitive on commonsense completion. They also uncover a tokenizer‑related evaluation pitfall that can drastically lower accuracy and provide a simple fix, releasing all code, logs, and corrected tokenizer for full reproducibility.
By Bruno Leonardo Santos Menezes, Carlos Leonardo Souza Cardoso, Fabio Andre Machado Porto
Brazilian Portuguese remains under-served by open language models, and the few that exist are difficult to reproduce and are often compared without measures of uncertainty. We release Manacá-1B, an op...
arXiv:2610.18420v1 Announce Type: cross
Abstract: We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, m...
By Ramchand Kumaresan, Madhusudhanan G, Kaushik Kezendirane
The paper compares two low‑budget methods for converting a 30B Mixture‑of‑Experts autoregressive language model into a diffusion language model. One method updates a subset of the model’s weights in‑place, while the other freezes the context tower and conditions on a frozen causal copy via cross‑attention. With only 1B training tokens, the frozen‑tower approach achieves a HumanEval pass@10 score of 71.60 versus 6.19 for the in‑place method, and retains 95% of the parent’s GSM8K and 99% of its MMLU‑Pro performance.
By Wentao Lu, Jesse Clark, Tianyu Zhu
arXiv:2609.13154v1 Announce Type: new
Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et...
By Shamin Chokshi