Peter J. Liu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 49

Top Topics

Extractive Summarization

Top Venues

Publications

Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. 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
Small-scale proxies for large-scale Transformer training instabilities. ICLR (2024)
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu
Statistical Rejection Sampling Improves Preference Optimization. ICLR (2024)
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
LiPO: Listwise Preference Optimization through Learning-to-Rank. CoRR (2024)
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy 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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Katie Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington
Scaling Exponents Across Parameterizations and Optimizers. CoRR (2024)
Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu
Improving Large Language Model Fine-tuning for Solving Math Problems. CoRR (2023)
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan
SMART: Sentences as Basic Units for Text Evaluation. ICLR (2023)
Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
Self-Evaluation Improves Selective Generation in Large Language Models. CoRR (2023)
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
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR (2023)
Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu
Calibrating Sequence likelihood Improves Conditional Language Generation. ICLR (2023)
Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
Self-Evaluation Improves Selective Generation in Large Language Models. ICBINB (2023)
Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, Peter J. Liu
SLiC-HF: Sequence Likelihood Calibration with Human Feedback. CoRR (2023)
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
Small-scale proxies for large-scale Transformer training instabilities. CoRR (2023)
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu
Statistical Rejection Sampling Improves Preference Optimization. CoRR (2023)
Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. EMNLP (Findings) (2023)
Jason Phang, Yao Zhao, Peter J. Liu
Investigating Efficiently Extending Transformers for Long Input Summarization. EMNLP (2023)
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
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR (2023)
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR (2023)
Jason Phang, Yao Zhao, Peter J. Liu
Investigating Efficiently Extending Transformers for Long Input Summarization. CoRR (2022)
Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR (2022)
Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu
Calibrating Sequence likelihood Improves Conditional Language Generation. CoRR (2022)
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR (2022)
Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan
SMART: Sentences as Basic Units for Text Evaluation. CoRR (2022)
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21 (2020)
Yao Zhao, Mohammad Saleh, Peter J. Liu
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization. CoRR (2020)
Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. ICML (2020)
Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. CoRR (2019)
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
Likelihood Ratios for Out-of-Distribution Detection. CoRR (2019)
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. CoRR (2019)
Ben Goodrich, Vinay Rao, Mohammad Saleh, Peter J. Liu
Assessing The Factual Accuracy of Generated Text. CoRR (2019)
Eric Chu, Peter J. Liu
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. ICML (2019)
Ben Goodrich, Vinay Rao, Peter J. Liu, Mohammad Saleh
Assessing The Factual Accuracy of Generated Text. KDD (2019)
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS (2019)
Ethan Steinberg, Peter J. Liu
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models. CoRR (2019)
Peter J. Liu, Yu-An Chung, Jie Ren
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. CoRR (2019)
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
Scalable and accurate deep learning with electronic health records. npj Digit. Medicine 1 (2018)
W. James Murdoch, Peter J. Liu, Bin Yu
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. ICLR (2018)
Eric Chu, Peter J. Liu
Unsupervised Neural Multi-document Abstractive Summarization. CoRR (2018)
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 Howell, Claire Cui, Greg Corrado, Jeff Dean
Scalable and accurate deep learning for electronic health records. CoRR (2018)
W. James Murdoch, Peter J. Liu, Bin Yu
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. CoRR (2018)
Peter J. Liu
Learning to Write Notes in Electronic Health Records. CoRR (2018)
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
Generating Wikipedia by Summarizing Long Sequences. CoRR (2018)
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
Generating Wikipedia by Summarizing Long Sequences. ICLR (Poster) (2018)
Abigail See, Peter J. Liu, Christopher D. Manning
Get To The Point: Summarization with Pointer-Generator Networks. ACL (1) (2017)
Prajit Ramachandran, Peter J. Liu, Quoc V. Le
Unsupervised Pretraining for Sequence to Sequence Learning. EMNLP (2017)
Abigail See, Peter J. Liu, Christopher D. Manning
Get To The Point: Summarization with Pointer-Generator Networks. CoRR (2017)
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck
Online and Linear-Time Attention by Enforcing Monotonic Alignments. ICML (2017)
Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck
Online and Linear-Time Attention by Enforcing Monotonic Alignments. CoRR (2017)
Prajit Ramachandran, Peter J. Liu, Quoc V. Le
Unsupervised Pretraining for Sequence to Sequence Learning. CoRR (2016)