20 citations · 29 across the 10 of their papers we have counts for
3 papers · 1 filter
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…
An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
Rahul Thomas, Louai Zahran, Erica Choi +3
Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of perfor…