Publications (4)
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models
April Yang, Jordan Tab, Parth Shah +1
The increasing reliance on large language models (LLMs) for diverse applications necessitates a thorough understanding of their robustness to adversarial perturbations and out-of-d…
FlexLLM: Token-Level Co-Serving of LLM Inference and Finetuning with SLO Guarantees
Gabriele Oliaro, Xupeng Miao, Xinhao Cheng +9
Finetuning large language models (LLMs) is essential for task adaptation, yet today's serving stacks isolate inference and finetuning on separate GPU clusters -- wasting resources…
AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding
Zikun Li, Zhuofu Chen, Remi Delacourt +11
Modern large language model (LLM) applications exhibit diverse service-level objectives (SLOs), from low-latency requirements in interactive coding assistants to more relaxed const…
Generalizations of the M&M Game
Snehesh Das, Steven J. Miller, Geremias Polanco +5
The MandM Game involves two players who begin with I1 and I2 MandM's. During each round, each player tosses a fair coin: if the coin lands heads, that player eats one MandM, and if…