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20192026
most citedScalable Extraction of Training Data from (Production) Language Models

83 citations · 221 across the 19 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Are you going to finish that? A Practical Study of the Partial Token Problem

Hao Xu, Alisa Liu, Jonathan Hayase +2

Language models (LMs) are trained over sequences of tokens, whereas users interact with LMs via text. This mismatch gives rise to the partial token problem, which occurs when a use…

cs.CL2026

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…

cs.CL2025

Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizations

Brian Siyuan Zheng, Alisa Liu, Orevaoghene Ahia +3

Modern tokenizers employ deterministic algorithms to map text into a single "canonical" token sequence, yet the same string can be encoded as many non-canonical tokenizations using…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2024★ 1 cited

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,…

cs.CL2024

Query-Based Adversarial Prompt Generation

Jonathan Hayase, Ema Borevkovic, Nicholas Carlini +2

Recent work has shown it is possible to construct adversarial examples that cause an aligned language model to emit harmful strings or perform harmful behavior. Existing attacks wo…