arXiv:2607. 19181v1 Announce Type: cross Abstract: Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires.
By Aixiu An, Michael Jungo, Eloi Eynard, Mark Drenhaus, Andreas Fischer, Jean Hennebert, S\'ebastien Rumley
arXiv:2603. 09803v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance.
By Tiehua Mei, Minxuan Lv, Leiyu Pan, Zhenpeng Su, Hongru Hou, Hengrui Chen, Ao Xu, Deqing Yang
arXiv:2607. 29287v1 Announce Type: cross Abstract: Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains.
By Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi
arXiv:2605.31378v2 Announce Type: replace
Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that...
By Renfei Dang, Xinye Wang, Zhejian Lai, Weilu Xu, Shimin Tao, Daimeng Wei, Min Zhang, Shujian Huang
The paper explores whether structured linguistic reasoning traces can improve low‑resource machine translation by guiding large language models (LLMs). It proposes a pipeline that automatically generates step‑by‑step reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar‑rule banks, and evaluates these traces in in‑context learning, supervised fine‑tuning, and reinforcement fine‑tuning on Xibe and Chintang. The results show that providing reliable reasoning traces at inference time significantly boosts translation quality, whereas using them as training data yields smaller, less consistent gains, indicating that LLMs can benefit from grammatical guidance but struggle to generate accurate analyses themselves.
By Renhao Pei, Yihong Liu, Sampo Pyysalo, Hinrich Sch\"utze, Shaoxiong Ji
arXiv:2609.13916v1 Announce Type: new
Abstract: We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same fo...
By Tom Kocmi, Alexandre B\'erard, Phil Blunsom, Samuel Cahyawijaya, Shaun Cassini, Nicholas Frosst, Ona de Gibert, Aidan Gomez, Nithya Govindarajan, Shun Kiyono, Olivia Lasche, Lawrence Rogers, Kelly Marchisio, Nikita Moghe, Yash More, Camila Moran-Hidalgo, Yiyang Nan, Michael Sachs, Trisha Starostina, Daan van Stigt, Spencer Rarrick, Sebastian Vincent, Ivan Zhang