18 citations · 32 across the 17 of their papers we have counts for
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cs.CL2023
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble
Zhe Liu, Ozlem Kalinli
Recent research has shown that language models have a tendency to memorize rare or unique token sequences in the training corpus. After deploying a model, practitioners might be as…
cs.CL2023★ 2 cited
Contextual Biasing of Named-Entities with Large Language Models
Chuanneng Sun, Zeeshan Ahmed, Yingyi Ma +4
This paper studies contextual biasing with Large Language Models (LLMs), where during second-pass rescoring additional contextual information is provided to a LLM to boost Automati…
cs.CL2023★ 2 cited
Recovering from Privacy-Preserving Masking with Large Language Models
Arpita Vats, Zhe Liu, Peng Su +5
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typica…