7 papers
Fast Byte Latent Transformer
Julie Kallini, Artidoro Pagnoni, Tomasz Limisiewicz +5
Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limited by slow, byte-by-byte auto…
Bolmo: Byteifying the Next Generation of Language Models
Benjamin Minixhofer, Tyler Murray, Tomasz Limisiewicz +6
Recent advances in generative AI have been largely driven by large language models (LLMs), deep neural networks that operate over discrete units called tokens. To represent text, t…
OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities
Sahil Verma, Keegan Hines, Jeff Bilmes +4
The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is…
Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models
Hila Gonen, Terra Blevins, Alisa Liu +2
Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discuss…
MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling
Tomasz Limisiewicz, Terra Blevins, Hila Gonen +2
A major consideration in multilingual language modeling is how to best represent languages with diverse vocabularies and scripts. Although contemporary text encoding methods cover…
Comparing Hallucination Detection Metrics for Multilingual Generation
Haoqiang Kang, Terra Blevins, Luke Zettlemoyer
While many hallucination detection techniques have been evaluated on English text, their effectiveness in multilingual contexts remains unknown. This paper assesses how well variou…