3 papers
cs.CL2025
AutoJudge: Judge Decoding Without Manual Annotation
Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov +3
We introduce AutoJudge, a method that accelerates large language model (LLM) inference with task-specific lossy speculative decoding. Instead of matching the original model output…
cs.CL2025
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
Zhuoming Chen, Avner May, Ruslan Svirschevski +4
As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged…
cs.LG2024
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
Philip Zmushko, Marat Mansurov, Ruslan Svirschevski +3
As deep learning models become larger and more expensive, many practitioners turn to fine-tuning APIs. These web services allow fine-tuning a model between two parties: the client…