7 papers
TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection
Tom Sander, Hongyan Chang, Tomáš SouÄek +10
We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity,…
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales +19
Children acquire language grounding with remarkable robustness from limited visuo-linguistic input in ways that surpass today's best large multimodal models. Recent research sugges…
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Belen Alastruey, Niyati Bafna +29
High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current sys…
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation
Mahi Luthra, Jiayi Shen, Maxime Poli +14
Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-super…
Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages
Omnilingual ASR team, Gil Keren, Artyom Kozhevnikov +30
Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages be…
LongTail-Swap: benchmarking language models' abilities on rare words
Robin Algayres, Charles-Ãric Saint-James, Mahi Luthra +6
Children learn to speak with a low amount of data and can be taught new words on a few-shot basis, making them particularly data-efficient learners. The BabyLM challenge aims at ex…