Percy Liang

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Percy Liang is a person.

See: Shallow Semantic Parsing, Natural Language Understanding System, SQuAD Benchmark.

References

2024

(Zhang, Ladhak et al., 2024) ⇒ Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto. (2024). “Benchmarking Large Language Models for News Summarization.” In: Transactions of the Association for Computational Linguistics, 12. doi:10.1162/tacl_a_00632

2023

(Park et al., 2023) ⇒ Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. (2023). “Generative Agents: Interactive Simulacra of Human Behavior.” doi:10.48550/arXiv.2304.03442
(Zhang, Ladhak et al., 2023) ⇒ Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B. Hashimoto. (2023). “Benchmarking Large Language Models for News Summarization.” arXiv preprint arXiv:2301.13848

2022

(Wei, Tay et al., 2022) ⇒ Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.. (2022). “Emergent Abilities of Large Language Models.” In: Transactions on Machine Learning Research, 08/2022 (TMLR).
(Liang, Bommasani et al., 2022) ⇒ Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. (2022). “Holistic Evaluation of Language Models.” doi:10.48550/arXiv.2211.09110

2021

(Bommasani et al., 2021) ⇒ Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W Thomas, Florian Tramèr, Rose E Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang. (2021). “On the Opportunities and Risks of Foundation Models.” arXiv preprint arXiv:2108.07258.

2016

(Rajpurkar et al., 2016) ⇒ Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. (2016). “SQuAD: 100,000+ Questions for Machine Comprehension of Text.” In: arXiv preprint arXiv:1606.05250.

2016

(Liang, 2016) ⇒ Percy Liang. (2016). “Learning Executable Semantic Parsers for Natural Language Understanding.” In: Communications of the ACM Journal, 59(9). doi:10.1145/2866568
- QUOTE: A long-standing goal of artificial intelligence (AI) is to build systems capable of understanding natural language.

2016

(Liang, 2016) ⇒ Percy Liang. (2016). “Querying Unnormalized and Incomplete Knowledge Bases." Invited Talk at the 5th Workshop on Automated Knowledge Base Construction (AKBC-2016).
- ABSTRACT: In an ideal world, one might construct a perfect knowledge base and use it to answer compositional queries. However, real-world knowledge bases are far from perfect---they can be inaccurate and incomplete. In this talk, I show two ways that we can cope with these imperfections by directly learning to answer queries on the imperfect knowledge base. First, we treat semi-structured web tables as an unnormalized knowledge base and perform semantic parsing on it to answer compositional questions. Second, we show how to embed an incomplete knowledge base to support compositional queries directly in vector space. Finally, we discuss some ideas for combining the best of both worlds.

2015

(Liang, 2015) ⇒ Percy Liang. (2013). “Natural Language Understanding: Foundations and State-of-the-Art." Tutorial at ICML-2015.
- ABSTRACT: Building systems that can understand human language — being able to answer questions, follow instructions, carry on dialogues — has been a long-standing challenge since the early days of AI. Due to recent advances in machine learning, there is again renewed interest in taking on this formidable task. A major question is how one represents and learns the semantics (meaning) of natural language, to which there are only partial answers. The goal of this tutorial is (i) to describe the linguistic and statistical challenges that any system must address; and (ii) to describe the types of cutting edge approaches and the remaining open problems. Topics include distributional semantics (e.g., word vectors), frame semantics (e.g., semantic role labeling), model-theoretic semantics (e.g., semantic parsing), the role of context, grounding, neural networks, latent variables, and inference. The hope is that this unified presentation will clarify the landscape, and show that this is an exciting time for the machine learning community to engage in the problems in natural language understanding.

2013

(Berant et al., 2013) ⇒ Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. (2013). “Semantic Parsing on Freebase from Question-Answer Pairs.” In: Proceedings of EMNLP (EMNLP-2013).

2009

(Liang, Jordan & Klein, 2009) ⇒ Percy Liang, Michael I. Jordan, and Dan Klein. (2009). “Learning Semantic Correspondences with Less Supervision.” In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL 2009).

2006

(Liang et al., 2006) ⇒ Percy Liang, Ben Taskar, and Dan Klein. (2006). “Alignment by Agreement.” In: Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. doi:10.3115/1220835.1220849

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