SwissGov-RSD is the first naturalistic cross-lingual document-level benchmark with human token-level semantic difference annotations, on which both LLMs and encoders show a large performance gap relative to simpler settings.
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Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models.
A human-LLM collaborative pipeline yields EspanStereo, a multi-country Spanish stereotype dataset that exposes region-specific biases in Spanish LLMs and diverges sharply from English-centric resources.
ALEE generates AMR-based English minimal pairs with fine-grained semantic shifts, translates them, and evaluates embedding models on 275+ languages to expose cross-lingual gaps linked to training data and tokenization.
AfriSUD supplies new SUD-annotated dependency treebanks for nine Sub-Saharan African languages and demonstrates that existing models exhibit clear limitations on their syntax.
Evaluation across 1.1 million instances shows sycophancy rates spike in low-resource languages, remain topic-agnostic, and correlate with tokenizer fertility.
A cycle-consistent MT pipeline generates and similarity-weights training data for coreference resolution, producing gains on four low-resource languages and enabling the task where no corpora existed.
SEA-Embedding is a fully open text embedding pipeline for Southeast Asian languages that achieves state-of-the-art performance on the SEA-BED benchmark by analyzing data composition, training objectives, and base encoder choices.
Low-resource safety failures are action failures because the harmfulness representation transfers but the decision calibration does not; this is fixed by recalibrating a high-resource gate with 1-4 target-language examples.
Training-language dominance, not English inherent properties, determines brain-LLM alignment across English, Chinese, and French, with additional independent effects from typological distance concentrated in syntactic brain regions.
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
A new corpus of 100,502 annotated movie reviews from Kazakhstan enables sentiment analysis research in Russian, Kazakh, and code-switched texts.
A framework with TOPPing source selection and VACAI-Bowl dual-branch model yields 54.62% average improvement in dependency parsing across 10 low-resource varieties.
Nsanku benchmark shows current LLMs achieve only modest zero-shot translation scores on 43 Ghanaian languages, with no model reaching both high average performance and high cross-language consistency.
Parallel-SFT mixes parallel programs across languages during SFT to produce more transferable RL initializations, yielding better zero-shot generalization to unseen programming languages.
C-Mining automatically mines high-fidelity Culture Points from raw multilingual text by treating cross-lingual geometric isolation in embeddings as a quantifiable signal for cultural specificity, then uses them to synthesize better instruction data.
WhaVax is a new expert-annotated dataset of WhatsApp vaccine messages with benchmarks showing competitive performance from embeddings and LLMs for misinformation detection under data scarcity.
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
A new multilingual corpus for Cham, Khmer, and Tay-Nung, plus a script-aware ELECTRA-style recipe, achieves the best topic-classification accuracy among the tested encoders — and shows why lexical-overlap retrieval can mislead low-resource benchmarks.
MultiSynt/MT supplies 4.8 trillion translated tokens in 36 languages from 100B English tokens, letting LLMs match native-data baselines with 72% fewer tokens and beat them by 15% at equal budget.
Transformers on impossible-language variants show gradual grammatical sensitivity loss but sharp long-sentence generation failures, supporting generative deficiency as a link to non-attestation.
A framework estimates grammatical gender directions in contextual embeddings via controlled and natural contexts, finding unweighted controlled contexts and centroid estimators yield the purest directions.
ToxiREX is a new dataset of 128k Reddit comments in six languages with hierarchical annotations for implicit toxicity in conversational context based on an existing reasoning schema.
Introduces Dango, a 1.8B strictly L1-only LLM using corpus filtering and lesson fine-tuning to simulate Japanese-to-English SLA and produce human-like L2 output patterns.
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ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression
A lightweight multilingual encoder system with joint training and adaptive ensemble achieves top-half rankings across datasets in SemEval-2026 dimensional aspect sentiment regression.