Rui Wang

Achieve near-perfect covert communication even when tokenizers disagree, by selectively patching up tokenization mismatches on the fly.

Split learning offers a surprisingly viable path to fine-tuning LLMs on sensitive data without breaking the bank or sacrificing privacy.

Object detection models are surprisingly vulnerable to practical backdoor attacks using real-world semantic triggers that work across different sizes, locations, and viewpoints.

Scaling visual preference optimization hinges on data quality, as demonstrated by the finding that standard DPO suffices for a sufficiently large and clean dataset, while a novel Poly-DPO objective is crucial for noisy data.