Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901

Earlier work this paper cites.

ROUGE: A package for automatic evaluation of summaries

Chin-Yew Lin. 2004 · 2004

Earlier work this paper cites.

On the translocation of masses

Leonid V Kantorovich. 2006 · 2006

Earlier work this paper cites.

The right to be forgotten

Jeffrey Rosen. 2011 · 2011

Earlier work this paper cites.

Sliced and radon wasserstein barycenters of measures

Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister. 2015 · 2015

Earlier work this paper cites.

Towards making systems forget with machine unlearning

Yinzhi Cao and Junfeng Yang. 2015 · 2015

Earlier work this paper cites.

The LAMBADA dataset: Word prediction requiring a broad discourse context

Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016

Earlier work this paper cites.

Think you have solved question answering? try arc, the ai2 reasoning challenge

Original

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018

Earlier work this paper cites.

Can a suit of armor conduct electricity? a new dataset for open book question answering

Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018

Earlier work this paper cites.

The california consumer privacy act: Towards a european-style privacy regime in the united states

Stuart L Pardau. 2018 · 2018

Earlier work this paper cites.

Learning dense representations for entity retrieval

Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, and Diego Garcia-Olano. 2019 · 2019

Earlier work this paper cites.

The european union general data protection regulation: what it is and what it means

Chris Jay Hoofnagle, Bart Van Der Sloot, and Frederik Zuiderveen Borgesius. 2019 · 2019

Earlier work this paper cites.

Decoupled weight decay regularization

Ilya Loshchilov and Frank Hutter. 2019 · 2019

Earlier work this paper cites.

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019

Earlier work this paper cites.

Sentence-bert: Sentence embeddings using siamese bert-networks

Original

Nils Reimers and Iryna Gurevych. 2019 · 2019

Earlier work this paper cites.

CommonsenseQA: A question answering challenge targeting commonsense knowledge

Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019

Earlier work this paper cites.

HellaSwag: Can a machine really finish your sentence?

Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019

Cited alongside, same era.

Piqa: Reasoning about physical commonsense in natural language

Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. 2020 · 2020

Cited alongside, same era.

Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations

Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020b · 2020

Cited alongside, same era.

Transformers: State-of-the-art natural language processing

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020

Cited alongside, same era.

Negative preference optimization: From catastrophic collapse to effective unlearning

Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. 2024 · 2020

Cited alongside, same era.

Towards unbounded machine unlearning

Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou. 2023 · 2023

Later among the works it cites.

When not to trust language models: Investigating effectiveness of parametric and non-parametric memories

Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023

Later among the works it cites.

FActScore: Fine-grained atomic evaluation of factual precision in long form text generation

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023

Later among the works it cites.

Instruction tuning with gpt-4

Original

Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023

Later among the works it cites.

Direct preference optimization: Your language model is secretly a reward model

Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023