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20232026
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cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +241

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

Scaling Unsupervised Word Alignment to Documents via Structural Constraints

Michelle Wastl, Jannis Vamvas, Rico Sennrich

Word alignment has traditionally been studied between sentences, but many cross-lingual tasks increasingly require correspondences across full documents. While recent multilingual…

cs.CL2026

Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

Hanxu Hu, Zdeněk Šnajdr, Pinzhen Chen +2

Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their c…

cs.CL2026

DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning

Hanxu Hu, Yuxuan Wang, Maggie Huan +4

Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics…

cs.CL2026

Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties

Jannis Vamvas, Ignacio Pérez Prat, Angela Heldstab +3

Recent strategies for low-resource machine translation rely on LLMs to generate synthetic data based on text in higher-resource languages. We revisit this idea for Romansh, a langu…

cs.CL2025

Expanding the WMT24++ Benchmark with Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader

Jannis Vamvas, Ignacio Pérez Prat, Not Battesta Soliva +14

The Romansh language, spoken in Switzerland, has limited resources for machine translation evaluation. In this paper, we present a benchmark for six varieties of Romansh: Rumantsch…