Peter J. Liu - RankMe

52 papers A* 13Journal 33Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	LiPO: Listwise Preference Optimization through Learning-to-Rank. NAACL (Long Papers) 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
2024	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	LiPO: Listwise Preference Optimization through Learning-to-Rank. CoRR 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
2024	A*	`conf`	Scaling Exponents Across Parameterizations and Optimizers. ICML Katie E. 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
2024	J	`jnl`	Scaling Exponents Across Parameterizations and Optimizers. CoRR 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
2024	A*	`conf`	Small-scale proxies for large-scale Transformer training instabilities. ICLR 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
2024	A*	`conf`	Statistical Rejection Sampling Improves Preference Optimization. ICLR Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu
2024	J	`jnl`	Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith
2023	J	`jnl`	Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR 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
2023	A*	`conf`	Calibrating Sequence likelihood Improves Conditional Language Generation. ICLR Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu
2023	J	`jnl`	Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR 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
2023	J	`jnl`	Improving Large Language Model Fine-tuning for Solving Math Problems. CoRR Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu
2023	—	`conf`	Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. EMNLP (Findings) Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
2023	A*	`conf`	Investigating Efficiently Extending Transformers for Long Input Summarization. EMNLP Jason Phang, Yao Zhao, Peter J. Liu
2023	A*	`conf`	Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
2023	J	`jnl`	SLiC-HF: Sequence Likelihood Calibration with Human Feedback. CoRR Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, Peter J. Liu
2023	A*	`conf`	SMART: Sentences as Basic Units for Text Evaluation. ICLR Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan
2023	—	`conf`	Self-Evaluation Improves Selective Generation in Large Language Models. ICBINB Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
2023	J	`jnl`	Self-Evaluation Improves Selective Generation in Large Language Models. CoRR Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
2023	J	`jnl`	Small-scale proxies for large-scale Transformer training instabilities. CoRR 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
2023	J	`jnl`	Statistical Rejection Sampling Improves Preference Optimization. CoRR Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu
2022	J	`jnl`	Calibrating Sequence likelihood Improves Conditional Language Generation. CoRR Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu
2022	J	`jnl`	Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu
2022	J	`jnl`	Investigating Efficiently Extending Transformers for Long Input Summarization. CoRR Jason Phang, Yao Zhao, Peter J. Liu
2022	J	`jnl`	Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu
2022	J	`jnl`	SMART: Sentences as Basic Units for Text Evaluation. CoRR Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan
2020	J	`jnl`	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
2020	A*	`conf`	PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. ICML Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu
2020	J	`jnl`	SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization. CoRR Yao Zhao, Mohammad Saleh, Peter J. Liu
2019	A*	`conf`	Assessing The Factual Accuracy of Generated Text. KDD Ben Goodrich, Vinay Rao, Peter J. Liu, Mohammad Saleh
2019	J	`jnl`	Assessing The Factual Accuracy of Generated Text. CoRR Ben Goodrich, Vinay Rao, Mohammad Saleh, Peter J. Liu
2019	J	`jnl`	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. CoRR Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
2019	A*	`conf`	Likelihood Ratios for Out-of-Distribution Detection. NeurIPS Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2019	J	`jnl`	Likelihood Ratios for Out-of-Distribution Detection. CoRR Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
2019	A*	`conf`	MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. ICML Eric Chu, Peter J. Liu
2019	J	`jnl`	PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. CoRR Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu
2019	J	`jnl`	SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. CoRR Peter J. Liu, Yu-An Chung, Jie Ren
2019	J	`jnl`	Using Ontologies To Improve Performance In Massively Multi-label Prediction Models. CoRR Ethan Steinberg, Peter J. Liu
2018	—	`conf`	Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. ICLR W. James Murdoch, Peter J. Liu, Bin Yu
2018	J	`jnl`	Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. CoRR W. James Murdoch, Peter J. Liu, Bin Yu
2018	—	`conf`	Generating Wikipedia by Summarizing Long Sequences. ICLR (Poster) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
2018	J	`jnl`	Generating Wikipedia by Summarizing Long Sequences. CoRR Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
2018	J	`jnl`	Learning to Write Notes in Electronic Health Records. CoRR Peter J. Liu
2018	J	`jnl`	Scalable and accurate deep learning for electronic health records. CoRR 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
2018	J	`jnl`	Scalable and accurate deep learning with electronic health records. npj Digit. Medicine 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
2018	J	`jnl`	Unsupervised Neural Multi-document Abstractive Summarization. CoRR Eric Chu, Peter J. Liu
2017	—	`conf`	Get To The Point: Summarization with Pointer-Generator Networks. ACL (1) Abigail See, Peter J. Liu, Christopher D. Manning
2017	J	`jnl`	Get To The Point: Summarization with Pointer-Generator Networks. CoRR Abigail See, Peter J. Liu, Christopher D. Manning
2017	A*	`conf`	Online and Linear-Time Attention by Enforcing Monotonic Alignments. ICML Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck
2017	J	`jnl`	Online and Linear-Time Attention by Enforcing Monotonic Alignments. CoRR Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck
2017	A*	`conf`	Unsupervised Pretraining for Sequence to Sequence Learning. EMNLP Prajit Ramachandran, Peter J. Liu, Quoc V. Le
2016	J	`jnl`	Unsupervised Pretraining for Sequence to Sequence Learning. CoRR Prajit Ramachandran, Peter J. Liu, Quoc V. Le