no code implementations • 16 Nov 2023 • Anirudh Ajith, Sameer Singh, Danish Pruthi
However, implanting such signals alters the model's output distribution and can have unintended effects when watermarked LLMs are used for downstream applications.
no code implementations • 25 Oct 2023 • Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer
Min-K% Prob can be applied without any knowledge about the pretraining corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data.
no code implementations • 1 Jul 2023 • Anirudh Ajith, Chris Pan, Mengzhou Xia, Ameet Deshpande, Karthik Narasimhan
In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations.
1 code implementation • 24 May 2023 • Alexis Chevalier, Alexander Wettig, Anirudh Ajith, Danqi Chen
Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents.