Fetching the paper…
Reading the bibliography…
To deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate context -- personal, demographic, and cultural information specific to an end-user.
How to grow a mind: Statistics, structure, and abstraction
Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman · 2011
Earlier work this paper cites.
Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Earlier work this paper cites.
Crowd-based personalized natural language explanations for recommendations
Shuo Chang, F. Maxwell Harper, and Loren Gilbert Terveen · 2016
Earlier work this paper cites.
Pragmatic language interpretation as probabilistic inference
Noah D Goodman and Michael C Frank · 2016
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering, 2018
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
Earlier work this paper cites.
Compact personalized models for neural machine translation, 2018
Joern Wuebker, Patrick Simianer, and John DeNero · 2018
Earlier work this paper cites.
Generating personalized recipes from historical user preferences, 2019
Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, and Julian McAuley · 2019
Earlier work this paper cites.
Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang · 2019
Earlier work this paper cites.
Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Understanding black-box predictions via influence functions, 2020
Pang Wei Koh and Percy Liang · 2020
Earlier work this paper cites.
Towards controllable and personalized review generation, 2020
Pan Li and Alexander Tuzhilin · 2020
Earlier work this paper cites.
Reducing non-normative text generation from language models
Xiangyu Peng, Siyan Li, Spencer Frazier, and Mark Riedl · 2020
Earlier work this paper cites.
Explainable recommendation: A survey and new perspectives
Yongfeng Zhang, Xu Chen, et al · 2020
Earlier work this paper cites.
PENS: A dataset and generic framework for personalized news headline generation
Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie · 2021
Earlier work this paper cites.
Latent hatred: A benchmark for understanding implicit hate speech
Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, and Diyi Yang · 2021
Cited alongside, same era.
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Cited alongside, same era.
Debiasing pre-trained contextualised embeddings
Masahiro Kaneko and Danushka Bollegala · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush · 2021
Cited alongside, same era.
What are you optimizing for? aligning recommender systems with human values
Contrastive decoding: Open-ended text generation as optimization, 2023
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis · 2023
Later among the works it cites.
Holistic evaluation of language models, 2023
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 · 2023
Later among the works it cites.
Engagement, user satisfaction, and the amplification of divisive content on social media, 2023
Smitha Milli, Micah Carroll, Yike Wang, Sashrika Pandey, Sebastian Zhao, and Anca D. Dragan · 2023
Later among the works it cites.
More human than human: measuring chatgpt political bias
Fabio Motoki, Valdemar Pinho Neto, and Victor Rangel · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonathan Stray, Ivan Vendrov, Jeremy Nixon, Steven Adler, and Dylan Hadfield-Menell · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model, May 2021
Ben Wang and Aran Komatsuzaki · 2021
Cited alongside, same era.
Estimating and penalizing induced preference shifts in recommender systems, 2022
Micah Carroll, Anca Dragan, Stuart Russell, and Dylan Hadfield-Menell · 2022
Cited alongside, same era.
Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection, 2022
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar · 2022
Cited alongside, same era.
Gender biases and where to find them: Exploring gender bias in pre-trained transformer-based language models using movement pruning
Przemyslaw Joniak and Akiko Aizawa · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
Cited alongside, same era.
Bbq: A hand-built bias benchmark for question answering, 2022
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman · 2022
Cited alongside, same era.
Extracting latent steering vectors from pretrained language models, 2022
Nishant Subramani, Nivedita Suresh, and Matthew E. Peters · 2022
Cited alongside, same era.
Contrastive decoding improves reasoning in large language models, 2023
Sean O’Brien and Mike Lewis · 2023
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model, 2023
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
Later among the works it cites.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom, 2023
2023
Later among the works it cites.
Activation addition: Steering language models without optimization, 2023
Alexander Matt Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid · 2023
Later among the works it cites.
Personalised language modelling of screen characters using rich metadata annotations, 2023
Sebastian Vincent, Rowanne Sumner, Alice Dowek, Charlotte Blundell, Emily Preston, Chris Bayliss, Chris Oakley, and Carolina Scarton · 2023
Later among the works it cites.
Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
Later among the works it cites.
Measuring implicit bias in explicitly unbiased large language models, 2024
Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L. Griffiths · 2024
Closest in time.
Olmo: Accelerating the science of language models, 2024
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hannaneh Hajishirzi · 2024
Closest in time.
Large language models are geographically biased, 2024
Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, and Stefano Ermon · 2024
Closest in time.
Lamp: When large language models meet personalization, 2024
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani · 2024
Closest in time.