Aneesh Pappu

According to our database, Aneesh Pappu authored at least 9 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation.

Alexander Xiong, Xuandong Zhao, Aneesh Pappu, Dawn Song

CoRR, July, 2025

Lessons from Defending Gemini Against Indirect Prompt Injections.

Chongyang Shi, Sharon Lin, Shuang Song, Jamie Hayes, Ilia Shumailov, Itay Yona, Juliette Pluto, Aneesh Pappu, Christopher A. Choquette-Choo, Milad Nasr, Chawin Sitawarin, Gena Gibson, Andreas Terzis, John Flynn

CoRR, May, 2025

Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity.

Sahra Ghalebikesabi, Eugene Bagdasarian, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle

Trans. Mach. Learn. Res., 2025

Measuring memorization in RLHF for code completion.

Jamie Hayes, Ilia Shumailov, William P. Porter, Aneesh Pappu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Operationalizing Contextual Integrity in Privacy-Conscious Assistants.

Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle

CoRR, 2024

Measuring memorization in RLHF for code completion.

Aneesh Pappu, Billy Porter, Ilia Shumailov, Jamie Hayes

CoRR, 2024

Scaling Instructable Agents Across Many Simulated Worlds.

SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young

CoRR, 2024

2020

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning.

Aneesh Pappu, Brooks Paige

CoRR, 2020

2019

Do Massively Pretrained Language Models Make Better Storytellers?

Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, Christopher D. Manning

Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019