TatBLiMP is the first benchmark of linguistic minimal pairs for the Tatar language, covering 16 morphosyntactic phenomena across 1,248 sentence pairs that differ by a single morpheme. Each pair contains one grammatical and one ungrammatical sentence, with the ungrammatical version generated by a deterministic perturbation and ratified by a native speaker. The benchmark evaluates models by comparing their assigned probabilities, allowing assessment without text generation or parsing, and tracks performance across from-scratch, cross‑lingual, and multilingual large language models.
By Ilshat Saetov, Dmitry Gaynullin
Viveka-Insight is a bilingual resource and open‑source pipeline for Swami Vivekananda’s complete works, providing a structure‑preserving parse of 32,694 paragraphs and 168,842 sentences, a cross‑lingual concept graph with 8,362 language‑agnostic concepts, a bilingual alias inventory of 60,850 surface forms, and a human‑annotated set of 200 paragraph‑concept edges. The resource enables citation‑grounded retrieval across the English and Bengali corpora, achieving Recall@10 of 0.86 for known‑item cross‑lingual queries and demonstrating concept‑extraction precision of 0.60 (up to 0.71 with confidence filtering). The design is intended to be transferable to other multilingual classical corpora.
By Tamal Maharaj
arXiv:2608.21821v1 Announce Type: new
Abstract: When Wikipedia's language editions describe the same concept, how differently do they frame it? Prior work measures coverage gaps between editions; we...
By Hung-Hsuan Chen
arXiv:2607. 20443v1 Announce Type: cross Abstract: We release GLAN-QnA-KR, a 303,581-row openly redistributable Korean instruction-QA corpus produced via the seedless taxonomy-driven GLAN synthesis pipeline with Microsoft's Phi-3.
By Daekeun Kim
arXiv:2609.12960v1 Announce Type: new
Abstract: Sanskrit fuses case, number, person and tense into word endings and chains clauses into compounds, so it is information-dense per word. Whether that de...
By Devansh Sharma
arXiv:2607. 23322v1 Announce Type: cross Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains.
By Shwetha Singaravelu, Gayathri Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani
arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.
By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani
arXiv:2609.13847v1 Announce Type: new
Abstract: In this paper, we extend FLORES+ with Portuguese-source evaluation sets for three Mozambican Bantu varieties: Xichangana, Mozambican Nyanja, and Sena....
By Felermino D. M. A. Ali, Delfina L\'azaro Mateus, Manuel Valente Mangue
arXiv:2608.30092v1 Announce Type: cross
Abstract: We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single...
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
The paper evaluates the tablet‑2 long‑term memory engine on multilingual text benchmarks and cross‑lingual photo retrieval without lexical matching. Tablet‑2 achieves high accuracy on LongMemEval‑S (95.7%) and moderate accuracy on BEAM‑1M (67.5%), with minimal variance across runs. In multimodal tests, it outperforms BM25 on image‑cell recall and shows significant language‑dependent performance gaps, especially for low‑resource languages.
By Sunwoo Kim
arXiv:2609.29090v1 Announce Type: cross
Abstract: We replicate the distributional-semantics extractive summarisation method of Mohd, Jan and Shah (2020) and adapt it to Hindi, substituting a Devanaga...
By Showket Ahmad Khan, Mudasir Mohd, Nasrullah Sheikh, Mohsin Altaf Wani, Abid Hussain Wani, Hilal Ahmad Khanday, Niyaz Ahmad Wani
The paper shows that the Flesch Reading Ease and Flesch‑Kincaid Grade Level scores, which are computed from the same two document statistics, converge almost surely to deterministic functions of a document’s topic distribution when modeled with a topic model that includes explicit sentence boundaries. In the long‑text limit, all variation in these scores is driven solely by topical composition, not by any residual readability signal. Experiments on the Brown and BNC corpora demonstrate that a topic vector inferred from one half of a document can predict the other half’s FKGL with substantial correlation (r = 0.779 and 0.884), though adding this prediction to genre and syllable‑count features yields only marginal gains in explained variance.
By Yo Ehara