1 citations · 1 across the 1 of their papers we have counts for
8 papers
Positional Embedding-Aware Activations
Kathan Shah, Chawin Sitawarin
We present a neural network architecture designed to naturally learn a positional embedding and overcome the spectral bias towards lower frequencies faced by conventional activatio…
Defending Against Prompt Injection With a Few DefensiveTokens
Sizhe Chen, Yizhu Wang, Nicholas Carlini +2
When large language model (LLM) systems interact with external data to perform complex tasks, a new attack, namely prompt injection, becomes a significant threat. By injecting inst…
Does More Inference-Time Compute Really Help Robustness?
Tong Wu, Chong Xiang, Jiachen T. Wang +4
Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-…
How much do language models memorize?
John X. Morris, Chawin Sitawarin, Chuan Guo +5
We propose a new method for estimating how much a model knows about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memori…
Lessons from Defending Gemini Against Indirect Prompt Injections
Chongyang Shi, Sharon Lin, Shuang Song +11
Gemini is increasingly used to perform tasks on behalf of users, where function-calling and tool-use capabilities enable the model to access user data. Some tools, however, require…
Mark My Words: Analyzing and Evaluating Language Model Watermarks
Julien Piet, Chawin Sitawarin, Vivian Fang +2
The capabilities of large language models have grown significantly in recent years and so too have concerns about their misuse. It is important to be able to distinguish machine-ge…