Tootfinder

Like coffee? Going to #FediCon? Meet me at Revolver Coffee at 325 Cambie St, Gastown between 730am and 8 and I'll buy you a coffee (Pink Crocs, wearing a pink baseball cap w/the phrase "Mangan Tamu" which is let's eat in Kapampangan, a Filipino language, one of 100s; Tagalog is the national language. Tagalog :: Kapampangan is like Mandarin :: Cantonese or something :-)

Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages
David Demitri Africa, Suchir Salhan, Yuval Weiss, Paula Buttery, Richard Diehl Martinez
https://arxiv.org/abs/2509.02160

Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages
Named-entity recognition (NER) in low-resource languages is usually tackled by finetuning very large multilingual LMs, an option that is often infeasible in memory- or latency-constrained settings. We ask whether small decoder LMs can be pretrained so that they adapt quickly and transfer zero-shot to languages unseen during pretraining. To this end we replace part of the autoregressive objective with first-order model-agnostic meta-learning (MAML). Tagalog and Cebuano are typologically similar …

FilBench: Can LLMs Understand and Generate Filipino?
Lester James V. Miranda, Elyanah Aco, Conner Manuel, Jan Christian Blaise Cruz, Joseph Marvin Imperial
https://arxiv.org/abs/2508.03523

FilBench: Can LLMs Understand and Generate Filipino?
Despite the impressive performance of LLMs on English-based tasks, little is known about their capabilities in specific languages such as Filipino. In this work, we address this gap by introducing FilBench, a Filipino-centric benchmark designed to evaluate LLMs across a diverse set of tasks and capabilities in Filipino, Tagalog, and Cebuano. We carefully curate the tasks in FilBench to reflect the priorities and trends of NLP research in the Philippines such as Cultural Knowledge, Classical NLP…