5 papers · 1 filter
Dynamic Delayed Tree Expansion For Improved Multi-Path Speculative Decoding
Rahul Thomas, Teo Kitanovski, Micah Goldblum +1
Multi-path speculative decoding accelerates lossless sampling from a target model by using a cheaper draft model to generate a draft tree of tokens, and then applies a verification…
Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actions
Arka Pal, Teo Kitanovski, Arthur Liang +2
Large language models (LLMs) are increasingly deployed in agentic and multi-turn workflows where they are tasked to perform actions of significant consequence. In order to deploy t…
Cascade: Token-Sharded Private LLM Inference
Rahul Thomas, Louai Zahran, Erica Choi +3
As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where…
vTune: Verifiable Fine-Tuning for LLMs Through Backdooring
Eva Zhang, Arka Pal, Akilesh Potti +1
As fine-tuning large language models (LLMs) becomes increasingly prevalent, users often rely on third-party services with limited visibility into their fine-tuning processes. This…
Large Language Models Must Be Taught to Know What They Don't Know
Sanyam Kapoor, Nate Gruver, Manley Roberts +7
When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is s…