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It is shown that the minimum dimensionality required for perfect multilinguality grows only logarithmically in the number of languages, which suggests that the empirical curse of multilinguality is a result of real world data and training conditions.
This work proposes a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems, using a large language model with Group Relative Policy Optimization (GRPO) and fine-tune a large language model with Group Relative Policy Optimiza...
A counterfactual condition (MIX) is test with a counterfactual condition (MIX) in which each document combines segments drawn from different systems, preserving document-level presentation while breaking cross-segment consistency.
The Last Translation Benchmark is introduced, a collection of human-authored and peer-reviewed examples that break leading machine translation models and a new evaluation approach: each example comes with handcrafted verification rules describing concrete failure cases on that example, therefore allowing reliable and a...
Pearmut is introduced, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation and enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort.
This work finds that self bias arises from two compounding sources, LLM as a testset and LLM as an evaluator, and their combination amplifies the effect and confirms that the phenomenon extends to open-ended generation on the Chatbot Arena task.
This work formalizes multi-model human evaluation as a best-arm identification problem in a multi-armed bandit setup with correlated arms, where pulling an arm corresponds to human-evaluating a model, and proves the optimality of the proposed algorithms and shows that it improves discrimination between top-performing m...
This work proposes augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator, and uses gradients from a combined difficulty and fluency objective to iteratively replace tokens in Adversarial Translation Optimization (AT...
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