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TACTICS is a method for selecting evaluation samples in machine translation that explicitly optimizes for coverage of rare linguistic categories, document-level coherence, and distributional fidelity to the full corpus. It builds a hierarchical taxonomy from a locale style guide, classifies segments, and chooses a fixed-budget subset that better represents the full range of phenomena a system must handle. Compared to random, lexical, or embedding-based selection, TACTICS improves coverage of rare categories and yields more accurate system rankings with fewer segments.
arXiv:2608. 13695v1 Announce Type: cross Abstract: Large language model providers routinely cite multilingual safety benchmarks spanning a dozen or more languages as evidence that their models are safe for non-English-speaking users.
arXiv:2602.01161v2 Announce Type: replace Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning...
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
The paper introduces MAPLE, a family of decoder‑only language models pretrained with document‑level geographic metadata such as source URL, country, and continent. MAPLE is evaluated on a new benchmark, LocalNewsQA, which tests whether models can switch answers when the locale changes. Experiments show that, with inference‑time metadata fixed, MAPLE outperforms metadata‑free controls in both answer switching and accuracy on locale‑dependent questions, and these gains grow with model size.
arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...