The paper reports that in English all‑words word sense disambiguation (WSD), the scarcity of high‑quality labels—not the models—has become the limiting factor. The authors introduce lexEN, a human‑adjudicated correction layer over the Maru2022 ALL_NEW benchmark, and SenseBench, a living leaderboard for LLM WSD evaluation. They show that frontier large language models reach about 95 % accuracy on lexEN‑v1, that relabeling corpora with these models improves downstream systems, and that fine‑grained WordNet senses are often ill‑posed, with coarsening improving both annotator agreement and model performance.
"whyItMatters":"The study highlights that improving label quality and managing annotation costs are now the critical challenges for advancing WSD performance, as model accuracy is already near its theoretical ceiling."
By Vassili Philippov, Amro Salman, Dmitrii Andreev, Penny Hands, Emil Kaiumov, Pavel Katunin, Anton Nikolaev
arXiv:2609.13611v1 Announce Type: new
Abstract: The WMT26 General MT task evaluates systems on 10 language pairs that have no human references (neither translated from scratch nor post-edited from MT...
By Diptesh Kanojia, Chi-kiu Lo, Archchana Sindhujan, Samuel Larkin, Greg Hanneman, Alon Lavie
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y
The paper introduces grounded glossary generation, a structured NLP task that asks models to recover semantically meaningful Sanskrit phrases and provide translation‑grounded meanings from a sloka‑translation pair, mirroring the traditional patha commentary practice. A benchmark of 31,316 sloka‑translation‑glossary triples from the Valmiki Ramayana and Srimad Bhagavatam is built, evaluated with Jaccard for phrase recovery and Meaning Faithfulness for semantic consistency. Experiments with Gemma‑3n‑E4B, Gemma‑3‑12B, Phi‑4, and Qwen3.5‑9B show that instruction fine‑tuning outperforms prompting, and explicit segmentation further improves results, though over‑segmentation of sandhi and samasa compounds remains the main error source, highlighting morphological modeling as a key bottleneck.
By Manoj Balaji Jagadeeshan, Sai Pragnaan Marala, Pawan Goyal
arXiv:2508.11857v3 Announce Type: replace-cross
Abstract: Tokenization remains a persistent bottleneck in language modeling, especially when vocabulary learning is limited by whitespace boundaries. W...
By Andrei-Valentin T\u{a}nase, Elena Pelican
arXiv:2606. 09767v1 Announce Type: cross Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping.
By Alexander Chulzhanov, Soeren Eberhardt, Arjun Mukherjee
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
The study investigates how post‑training quantization (PTQ) affects proactive interference (PI) in large language models. Using bitsandbytes, the authors compare FP16, INT8, and INT4/NF4 precision across three instruction‑tuned models and find that INT4 quantization markedly degrades accuracy under high interference, with INT8 also incurring a smaller penalty in two of the three models. The degradation is linked to increased same‑key intrusion errors and originates in the quantized transformer backbone rather than the output layer.
By Shayan Shahrabi-Farahani (Shahid Beheshti University, Tehran, Iran), Dara Rahmati (Shahid Beheshti University, Tehran, Iran)
The paper introduces discourse dependency (DDP) as a continuous measure of translation difficulty based on how far back a segment must look to resolve references. DDP is computed from named entity re‑mentions and pronominal coreference, and is validated against gold coreference with high reliability. Applying DDP to recent WMT benchmarks reveals a bias toward low‑DDP segments, and experiments show that as DDP increases, no current context‑injection strategy matches human post‑editing quality.
By Ahrii Kim, Chanjun Park, Seong-heum Kim
arXiv:2609.18156v1 Announce Type: new
Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
By Jianan Wu
arXiv:2607. 09957v1 Announce Type: cross Abstract: This report studies on-device English-to-Traditional-Chinese subtitle translation for Taiwan under short inputs, short outputs, batch-size-one inference, low latency, and privacy constraints.
By Tsz-To Wong
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