LAMB: A Modern Contextual Language Model for Latin

We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question-answer pairs. The questions are drawn from Latin pedagogical sources, including exams, quizbowl-style trivia, and textbooks ranging from the 1800s to the present. After automated extraction, cleaning, and manual review, the dataset covers a diverse range of question types: knowledge- and skill-based, multihop reasoning, constrained translation, and mixed language pairs. To our knowledge, this is the first QA benchmark centered on Latin. As a case study, we evaluate three large language models -- LLaMa 3, Qwen QwQ, and OpenAI's o3-mini -- finding that all perform worse on skill-oriented questions. Although the reasoning models perform better on scansion and literary-device tasks, they offer limited improvement overall. QwQ performs slightly better on questions asked in Latin, but LLaMa3 and o3-mini are more task dependent. This dataset provides a new resource for assessing model capabilities in a specialized linguistic and cultural domain, and the creation process can be easily adapted for other languages. The dataset is available at: https://github.com/slanglab/RespondeoQA

Encoder-only transformers remain essential for practical NLP tasks. While recent advances in multilingual models have improved cross-lingual capabilities, low-resource languages such as Latvian remain underrepresented in pretraining corpora, and few monolingual Latvian encoders currently exist. We address this gap by pretraining a suite of Latvian-specific encoders based on RoBERTa, DeBERTaV3, and ModernBERT architectures, including long-context variants, and evaluating them across a diverse set of Latvian diagnostic and linguistic benchmarks. Our models are competitive with existing monolingual and multilingual encoders while benefiting from recent architectural and efficiency advances. Our best model, lv-deberta-base (111M parameters), achieves the strongest overall performance, outperforming larger multilingual baselines and prior Latvian-specific encoders. We release all pretrained models and evaluation resources to support further research and practical applications in Latvian NLP.

The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models remain the standard tool for classification, tagging and retrieval, where contextual representations and low inference cost matter more than text generation, yet Turkish lacks a monolingual encoder trained from scratch with the advances consolidated in ModernBERT (rotary positional embeddings, FlashAttention, refined normalization). We introduce TabiBERT, a monolingual Turkish encoder based on the ModernBERT architecture, pretrained from scratch for one trillion tokens sampled from an 86.58B-token multi-domain corpus of web text (72%), scientific publications (19%), source code (6%) and mathematical content (0.3%). The model supports a context length of 8,192 tokens, sixteen times that of existing Turkish BERT models, and inherits the ModernBERT architecture's efficiency at long context. For rigorous and reproducible evaluation we introduce TabiBench, a benchmark of 27 datasets across eight task categories with standardized splits and evaluation protocols, summarized as a GLUE-style macro-average on a 0-100 scale. TabiBERT leads the Turkish models in five of eight categories and BERTurk, the previous best, in six of eight; the gains concentrate on question answering (+9.55 F1) and code retrieval (+2.41 NDCG@10), while the four short-text categories are near saturation. Its average of 77.28 exceeds BERTurk's 75.66; the multilingual mmBERT reaches 78.98 with twice the parameters and three times the training tokens, at 41% more tokens per Turkish input. We release model weights, training configurations and evaluation code as a transparent and reproducible foundation for future Turkish encoder research.