Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Models

Investigating prior knowledge for challenging chinese machine reading comprehension

Original

Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2019 · 1904

Earlier work this paper cites.

Hellaswag: Can a machine really finish your sentence?

Original

Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 1905

Earlier work this paper cites.

Winogrande: An adversarial winograd schema challenge at scale

Original

Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907

Earlier work this paper cites.

Rank analysis of incomplete block designs: I. the method of paired comparisons

Ralph Allan Bradley and Milton E Terry. 1952 · 1952

Earlier work this paper cites.

Direct self-control (dsc) of inverter-fed induction machine

M. Depenbrock. 1988 · 1988

Earlier work this paper cites.

Cmos schmitt trigger design

I.M. Filanovsky and H. Baltes. 1994 · 1994

Earlier work this paper cites.

Measuring massive multitask language understanding

Original

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2009

Earlier work this paper cites.

Vqa: Visual question answering

Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015

Earlier work this paper cites.

Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017

Earlier work this paper cites.

Race: Large-scale reading comprehension dataset from examinations

Original

Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017

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.

Commonsenseqa: A question answering challenge targeting commonsense knowledge

Original

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

Earlier work this paper cites.

Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020

Earlier work this paper cites.

Learning to summarize from human feedback

Noam Stiennon, Long Ouyang, Jeff Wu, Coline Hesse, John Schulman, Paul Christiano, and Dario Amodei. 2020 · 2020

Earlier work this paper cites.

A general language assistant as a laboratory for alignment

Original

Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021 · 2021

Earlier work this paper cites.

On the dangers of stochastic parrots: Can language models be too big?

Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021

Earlier work this paper cites.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021

Earlier work this paper cites.

Ethical and social risks of harm from language models

Original

Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. 2021 · 2021

Earlier work this paper cites.

Training a helpful and harmless assistant with reinforcement learning from human feedback

Original

Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022

Earlier work this paper cites.

Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned

Original

Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al. 2022 · 2022

Cited alongside, same era.

Training language models to follow instructions with human feedback

Original

Long Ouyang, Jeff Wu, Xuechen Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022

Cited alongside, same era.

Red teaming language models with language models

Original

Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022 · 2022

Cited alongside, same era.

Challenging big-bench tasks and whether chain-of-thought can solve them

Original

Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei. 2022 · 2022

Secrets of rlhf in large language models part i: Ppo

Original

Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, et al. 2023 · 2023

Later among the works it cites.

Universal and transferable adversarial attacks on aligned language models

Original

Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023 · 2023

Later among the works it cites.

A general theoretical paradigm to understand learning from human preferences

Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. 2024 · 2024

Later among the works it cites.

Safe RLHF: Safe reinforcement learning from human feedback

Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. 2024 · 2024

Later among the works it cites.

Kto: Model alignment as prospect theoretic optimization

Original