Peter J. Liu

According to our database, Peter J. Liu authored at least 30 papers between 2017 and 2024.

Collaborative distances:

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

2024

LiPO: Listwise Preference Optimization through Learning-to-Rank.

Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J. Liu, Xuanhui Wang

CoRR, 2024

2023

Self-Evaluation Improves Selective Generation in Large Language Models.

Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan

CoRR, 2023

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

CoRR, 2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein

CoRR, 2023

Improving Large Language Model Fine-tuning for Solving Math Problems.

Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu

CoRR, 2023

Small-scale proxies for large-scale Transformer training instabilities.

Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

CoRR, 2023

Statistical Rejection Sampling Improves Preference Optimization.

Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu

CoRR, 2023

SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, Peter J. Liu

CoRR, 2023

Calibrating Sequence likelihood Improves Conditional Language Generation.

Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

SMART: Sentences as Basic Units for Text Evaluation.

Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Out-of-Distribution Detection and Selective Generation for Conditional Language Models.

Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Investigating Efficiently Extending Transformers for Long Input Summarization.

Jason Phang, Yao Zhao, Peter J. Liu

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise.

Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2020

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

J. Mach. Learn. Res., 2020

SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization.

Yao Zhao, Mohammad Saleh, Peter J. Liu

CoRR, 2020

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.

Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders.

Peter J. Liu, Yu-An Chung, Jie Ren

CoRR, 2019

Using Ontologies To Improve Performance In Massively Multi-label Prediction Models.

Ethan Steinberg, Peter J. Liu

CoRR, 2019

Likelihood Ratios for Out-of-Distribution Detection.

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Assessing The Factual Accuracy of Generated Text.

Ben Goodrich, Vinay Rao, Peter J. Liu, Mohammad Saleh

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization.

Eric Chu, Peter J. Liu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Scalable and accurate deep learning with electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean

npj Digit. Medicine, 2018

Unsupervised Neural Multi-document Abstractive Summarization.

Eric Chu, Peter J. Liu

CoRR, 2018

Learning to Write Notes in Electronic Health Records.

Peter J. Liu

CoRR, 2018

Scalable and accurate deep learning for electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg Corrado, Jeff Dean

CoRR, 2018

Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs.

W. James Murdoch, Peter J. Liu, Bin Yu

Proceedings of the 6th International Conference on Learning Representations, 2018

Generating Wikipedia by Summarizing Long Sequences.

Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Online and Linear-Time Attention by Enforcing Monotonic Alignments.

Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck

Proceedings of the 34th International Conference on Machine Learning, 2017

Unsupervised Pretraining for Sequence to Sequence Learning.

Prajit Ramachandran, Peter J. Liu, Quoc V. Le

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Get To The Point: Summarization with Pointer-Generator Networks.

Abigail See, Peter J. Liu, Christopher D. Manning

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017