7 papers
A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage
Rui Xin, Niloofar Mireshghallah, Shuyue Stella Li +6
Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately…
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…
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…
Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index
Hao Xu, Jiacheng Liu, Yejin Choi +2
Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable s…
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…
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens
Jiacheng Liu, Taylor Blanton, Yanai Elazar +28
We present OLMoTrace, the first system that traces the outputs of language models back to their full, multi-trillion-token training data in real time. OLMoTrace finds and shows ver…