74 citations · 81 across the 10 of their papers we have counts for
4 papers · 1 filter
Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model
Jacqueline He, Jonathan Hayase, Wen-tau Yih +3
Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyright-protected, such reproduction…
Sampling from Your Language Model One Byte at a Time
Jonathan Hayase, Alisa Liu, Noah A. Smith +1
Tokenization is used almost universally by modern language models, enabling efficient text representation using multi-byte or multi-character tokens. However, prior work has shown…
SuperBPE: Space Travel for Language Models
Alisa Liu, Jonathan Hayase, Valentin Hofmann +3
The assumption across nearly all language model (LM) tokenization schemes is that tokens should be subwords, i.e., contained within word boundaries. While providing a seemingly rea…
Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?
Jonathan Hayase, Alisa Liu, Yejin Choi +2
The pretraining data of today's strongest language models is opaque; in particular, little is known about the proportions of various domains or languages represented. In this work,…