Get To The Point: Summarization with Pointer-Generator Networks. - Abigail See; Peter J. Liu; Christopher D. Manning (ACL (1), 2017)

Unsupervised Pretraining for Sequence to Sequence Learning. - Prajit Ramachandran; Peter J. Liu; Quoc V. Le (EMNLP, 2017)

Online and Linear-Time Attention by Enforcing Monotonic Alignments. - Colin Raffel; Minh-Thang Luong; Peter J. Liu; Ron J. Weiss; Douglas Eck (ICML, 2017)

Generating Wikipedia by Summarizing Long Sequences. - Peter J. Liu; Mohammad Saleh; Etienne Pot; Ben Goodrich; Ryan Sepassi; Lukasz Kaiser; Noam Shazeer (ICLR (Poster), 2018)

Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. - W. James Murdoch; Peter J. Liu; Bin Yu 001 (ICLR, 2018)

Scalable and accurate deep learning for electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Peter J. Liu; Xiaobing Liu; Mimi Sun; Patrik Sundberg; Hector Yee; Kun Zhang 043; Gavin E. Duggan; et al. (CoRR, 2018)

Learning to Write Notes in Electronic Health Records. - Peter J. Liu (CoRR, 2018)

Unsupervised Neural Multi-document Abstractive Summarization. - Eric Chu; Peter J. Liu (CoRR, 2018)

Scalable and accurate deep learning with electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Michaela Hardt; Peter J. Liu; Xiaobing Liu; Jake Marcus; Mimi Sun; Patrik Sundberg; Hector Yee; et al. (npj Digit. Medicine, 2018)

MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. - Eric Chu; Peter J. Liu (ICML, 2019)

Assessing The Factual Accuracy of Generated Text. - Ben Goodrich; Vinay Rao; Peter J. Liu; Mohammad Saleh (KDD, 2019)

Likelihood Ratios for Out-of-Distribution Detection. - Jie Ren 006; Peter J. Liu; Emily Fertig; Jasper Snoek; Ryan Poplin; Mark A. DePristo; Joshua V. Dillon; Balaji Lakshminarayanan (NeurIPS, 2019)

Using Ontologies To Improve Performance In Massively Multi-label Prediction Models. - Ethan Steinberg; Peter J. Liu (CoRR, 2019)

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. - Peter J. Liu; Yu-An Chung; Jie Ren 006 (CoRR, 2019)

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. - Jingqing Zhang; Yao Zhao; Mohammad Saleh; Peter J. Liu (ICML, 2020)

SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization. - Yao Zhao; Mohammad Saleh; Peter J. Liu (CoRR, 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 133; Peter J. Liu (J. Mach. Learn. Res., 2020)

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. - Kundan Krishna; Yao Zhao; Jie Ren 006; Balaji Lakshminarayanan; Jiaming Luo; Mohammad Saleh; Peter J. Liu (EMNLP (Findings), 2023)

Investigating Efficiently Extending Transformers for Long Input Summarization. - Jason Phang; Yao Zhao; Peter J. Liu (EMNLP, 2023)

Self-Evaluation Improves Selective Generation in Large Language Models. - Jie Ren 006; Yao Zhao; Tu Vu; Peter J. Liu; Balaji Lakshminarayanan (ICBINB, 2023)

Out-of-Distribution Detection and Selective Generation for Conditional Language Models. - Jie Ren 006; Jiaming Luo; Yao Zhao; Kundan Krishna; Mohammad Saleh; Balaji Lakshminarayanan; Peter J. Liu (ICLR, 2023)

SMART: Sentences as Basic Units for Text Evaluation. - Reinald Kim Amplayo; Peter J. Liu; Yao Zhao; Shashi Narayan (ICLR, 2023)

Calibrating Sequence likelihood Improves Conditional Language Generation. - Yao Zhao; Misha Khalman; Rishabh Joshi; Shashi Narayan; Mohammad Saleh; Peter J. Liu (ICLR, 2023)

SLiC-HF: Sequence Likelihood Calibration with Human Feedback. - Yao Zhao; Rishabh Joshi; Tianqi Liu 002; Misha Khalman; Mohammad Saleh; Peter J. Liu (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)

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; et al. (CoRR, 2023)

Statistical Rejection Sampling Improves Preference Optimization. - Tianqi Liu 002; Yao Zhao; Rishabh Joshi; Misha Khalman; Mohammad Saleh; Peter J. Liu; Jialu Liu (ICLR, 2024)

Small-scale proxies for large-scale Transformer training instabilities. - 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; et al. (ICLR, 2024)

Scaling Exponents Across Parameterizations and Optimizers. - 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 001; Jeffrey Pennington (ICML, 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - 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; et al. (CoRR, 2024)

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 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)

LiPO: Listwise Preference Optimization through Learning-to-Rank. - Tianqi Liu 002; Zhen Qin 001; Junru Wu; Jiaming Shen; Misha Khalman; Rishabh Joshi; Yao Zhao; Mohammad Saleh; Simon Baumgartner; Jialu Liu; Peter J. Liu; Xuanhui Wang (NAACL (Long Papers), 2025)