Inspicio is an open‑vocabulary pipeline that links tokens in historical or low‑resource languages to synsets in the Open English WordNet without needing a source‑language sense inventory. It uses an instruction‑tuned LLM to generate two English translations, candidate dictionary definitions, and English lemmas, then performs hybrid retrieval combining dense definition similarity, sparse lemma matching, and Maximal Marginal Relevance re‑ranking. Evaluated on Latin, Ancient Greek, PREMOVE, and Italian data, the best configuration achieves 96% Recall@50 on a perception‑verb test set and remains competitive in out‑of‑domain and cross‑lingual scenarios.
By Michele Ciletti
arXiv:2604. 14397v2 Announce Type: replace-cross Abstract: We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation.
By David Basil, Chirooth Girigowda, Bradley Hauer, Sahir Momin, Ning Shi, Grzegorz Kondrak
The paper introduces Loci Similes, a benchmark for detecting intertextual links in Latin literature. It provides a curated dataset of about 176,000 text segments and 1,490 expert-verified parallels, including 945 labeled references from an existing source. Baselines for retrieval and classification are established using both lexical methods and pretrained encoder language models.
By Julian Schelb, Michael Wittweiler, Marie Revellio, Barbara Feichtinger, Andreas Spitz
arXiv:2608. 05138v1 Announce Type: cross Abstract: Modern Greek is absent from NVIDIA's Nemotron retrieval models and from major multilingual retrieval benchmarks, despite being important for retrieval-augmented generation (RAG) in legal, energy, financial, and medical applications.
By Ayoub Kirouane, Christos Petrocheilos
The paper introduces the problem of cross‑lingual loopholes in large language model (LLM) unlearning, where forgetting a fact in one language can leave it accessible in others. It presents a new 174‑language benchmark, the Cross‑Lingual Unlearning Tensor, and proposes COVER, a method that selects a subset of source languages to maximize unlearning coverage under a language budget. Experiments show COVER reduces residual knowledge by 7.8–27.3% compared to uniform selection and works on both synthetic and real low‑resource news data.
By Tyler Skow, Shravan Chaudhari, Rama Chellappa, Abhay Yadav
The paper introduces a three‑stage training pipeline that builds compact, efficient dense retrievers without requiring ground‑truth relevance labels. Using cross‑lingual alignment, relational knowledge distillation, and contrastive fine‑tuning, the authors develop PolDense (six Polish models ranging from 17 M to 1 B parameters) and EuroDense (a 435 M‑parameter model covering nine European languages). Extensive evaluation on 41 Polish and 150 multilingual tasks shows that PolDense‑1B outperforms larger retrievers up to 9 B parameters, while EuroDense leads in task‑averaged and language‑averaged performance among models below 1 B parameters.
By S{\l}awomir Dadas, Rafa{\l} Po\'swiata, Ma{\l}gorzata Gr\k{e}bowiec, Micha{\l} Pere{\l}kiewicz
arXiv:2608.28776v1 Announce Type: new
Abstract: Multilingual sentence embeddings are increasingly used to estimate semantic similarity across languages, yet their sensitivity to fine-grained translat...
By Eleftherios Kalogeros, Athanasios Ntalakas, Manolis Gergatsoulis, Paschalis Nikolaou, Sotiria-Lito Alexaki
arXiv:2607. 04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world.
By Lucas Hideki Takeuchi Okamura, Alexandre Alcoforado, Anna Helena Reali Costa
State-of-the-art retrieval models increasingly rely on closed training data, creating a reproducibility gap. We present an open end-to-end recipe for training retrieval models and study how English supervision transfers to multilingual retrieval through translate-train.
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli
arXiv:2607.00171v2 Announce Type: replace
Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...
By Andrianos Michail, Stylianos Psychias, Michelle Wastl, Simon Clematide, Rico Sennrich, Juri Opitz
arXiv:2606. 31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora.
By Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica