Fetching the paper…
Reading the bibliography…
The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving.
I. Robot
Isaac Asimov · 1940
Earlier work this paper cites.
Society of mind
Marvin Minsky · 1988
Earlier work this paper cites.
Kqml as an agent communication language
Tim Finin, Richard Fritzson, Don McKay, and Robin McEntire · 1994
Earlier work this paper cites.
Temporal difference learning and td-gammon
Gerald Tesauro et al · 1995
Earlier work this paper cites.
Multi-agent reinforcement learning: Independent versus cooperative agents
Ming Tan · 1997
Earlier work this paper cites.
The dynamics of reinforcement learning in cooperative multiagent systems
Caroline Claus and Craig Boutilier · 1998
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
Earlier work this paper cites.
The emotion machine: Commonsense thinking, artificial intelligence, and the future of the human mind
Marvin Minsky · 2007
Earlier work this paper cites.
Specifying protocols for multi-agent systems interaction
Stefan Poslad · 2007
Earlier work this paper cites.
An introduction to multiagent systems
Michael Wooldridge · 2009
Earlier work this paper cites.
Artificial intelligence a modern approach
Stuart J Russell · 2010
Earlier work this paper cites.
Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Alignment-based compositional semantics for instruction following
Jacob Andreas and Dan Klein · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
Earlier work this paper cites.
Cooperative inverse reinforcement learning
Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Emergence of language with multi-agent games: Learning to communicate with sequences of symbols
Serhii Havrylov and Ivan Titov · 2017
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Earlier work this paper cites.
Neural approaches to conversational ai
Jianfeng Gao, Michel Galley, and Lihong Li · 2018
Earlier work this paper cites.
Ethical challenges in data-driven dialogue systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, and Joelle Pineau · 2018
Earlier work this paper cites.
Paraphrasing complex network: Network compression via factor transfer
Jangho Kim, Seonguk Park, and Nojun Kwak · 2018
Earlier work this paper cites.
Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel · 2018
Earlier work this paper cites.
Legible normativity for ai alignment: The value of silly rules
Dylan Hadfield-Menell, McKane Andrus, and Gillian Hadfield · 2019
Earlier work this paper cites.
Knowledge transfer via distillation of activation boundaries formed by hidden neurons
Byeongho Heo, Minsik Lee, Sangdoo Yun, and Jin Young Choi · 2019
Earlier work this paper cites.
Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Imitating interactive intelligence, 2020
Josh Abramson, Arun Ahuja, Iain Barr, Arthur Brussee, Federico Carnevale, Mary Cassin, Rachita Chhaparia, Stephen Clark, Bogdan Damoc, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy Lillicrap, Kory Mathewson, Soňa Mokrá, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Duncan Williams, Nathaniel Wong, Chen Yan, and Rui Zhu · 2020
Earlier work this paper cites.
The hanabi challenge: A new frontier for ai research
Nolan Bard, Jakob N Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, et al · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Extracting training data from large language models. arxiv
N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, A Roberts, T Brown, D Song, Ú Erlingsson, et al · 2020
Earlier work this paper cites.
Open problems in cooperative ai
Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R McKee, Joel Z Leibo, Kate Larson, and Thore Graepel · 2020
Earlier work this paper cites.
Artificial intelligence, values, and alignment
Iason Gabriel · 2020
Earlier work this paper cites.
A simple language model for task-oriented dialogue
Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, and Richard Socher · 2020
Earlier work this paper cites.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 2020
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
Earlier work this paper cites.
Aligning ai optimization to community well-being
Jonathan Stray · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Earlier work this paper cites.
Grounded conversation generation as guided traverses in commonsense knowledge graphs
Houyu Zhang, Zhenghao Liu, Chenyan Xiong, and Zhiyuan Liu · 2020
Earlier work this paper cites.
Dialogue distillation: Open-domain dialogue augmentation using unpaired data
Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao, Xiao-Xi Mao, Yadong Xi, and Minlie Huang · 2020
Earlier work this paper cites.
Cross-layer distillation with semantic calibration
Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, and Chun Chen · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Cooperative ai: machines must learn to find common ground
Allan Dafoe, Yoram Bachrach, Gillian Hadfield, Eric Horvitz, Kate Larson, and Thore Graepel · 2021
Cited alongside, same era.
Learning correlated communication topology in multi-agent reinforcement learning
Yali Du, Bo Liu, Vincent Moens, Ziqi Liu, Zhicheng Ren, Jun Wang, Xu Chen, and Haifeng Zhang · 2021
Cited alongside, same era.
A framework for few-shot language model evaluation, September 2021
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2021
WANLI: Worker and AI collaboration for natural language inference dataset creation
Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi · 2022
Later among the works it cites.
The alignment problem: Machine learning and human values
Michael J. Matthews, Samuel H. Matthews, and Thomas K. Kelemen · 2022
Later among the works it cites.
Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han · 2022
Later among the works it cites.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2022
Later among the works it cites.
Introducing chatgpt
OpenAI · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The principal-agent alignment problem in artificial intelligence
Dylan Hadfield-Menell · 2021
Cited alongside, same era.
Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
Cited alongside, same era.
Lila: Language-informed latent actions
Siddharth Karamcheti, Megha Srivastava, Percy Liang, and Dorsa Sadigh · 2021
Cited alongside, same era.
Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, and Geoffrey Irving · 2021
Cited alongside, same era.
Linda: Unsupervised learning to interpolate in natural language processing
Yekyung Kim, Seohyeong Jeong, and Kyunghyun Cho · 2021
Cited alongside, same era.
Augpt: Auxiliary tasks and data augmentation for end-to-end dialogue with pre-trained language models
Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, and Ondřej Dušek · 2021
Cited alongside, same era.
Neural data augmentation via example extrapolation
Kenton Lee, Kelvin Guu, Luheng He, Tim Dozat, and Hyung Won Chung · 2021
Cited alongside, same era.
Later among the works it cites.
A generalist agent, 2022
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
Later among the works it cites.
Andy Rosenbaum, Saleh Soltan, Wael Hamza, Yannick Versley, and Markus Boese · 2022
Later among the works it cites.
Data augmentation for intent classification with off-the-shelf large language models
Gaurav Sahu, Pau Rodriguez, Issam H Laradji, Parmida Atighehchian, David Vazquez, and Dzmitry Bahdanau · 2022
Later among the works it cites.
Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
Later among the works it cites.
Learning structured communication for multi-agent reinforcement learning
Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, and Hongyuan Zha · 2022
Later among the works it cites.
Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage
Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju, Eric Michael Smith, Stephen Roller, Megan Ung, Moya Chen, Kushal Arora, Joshua Lane, et al · 2022
Later among the works it cites.
Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
Later among the works it cites.
Self-instruct: Aligning language model with self generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
Later among the works it cites.
Super-naturalinstructions:generalization via declarative instructions on 1600+ tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al · 2022
Later among the works it cites.
Emergent abilities of large language models
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
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
Later among the works it cites.
Star: Bootstrapping reasoning with reasoning, 2022
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D. Goodman · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, and Ed Chi · 2022
Later among the works it cites.
Introducing claude
Anthropic · 2023
Closest in time.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Closest in time.
Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
Closest in time.
Places: Prompting language models for social conversation synthesis
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur · 2023
Closest in time.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Closest in time.
Josh A Goldstein, Girish Sastry, Micah Musser, Renee DiResta, Matthew Gentzel, and Katerina Sedova · 2023
Closest in time.
Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava · 2023
Closest in time.
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
Closest in time.
Cheap talk discovery and utilization in multi-agent reinforcement learning
Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, and Shimon Whiteson · 2023
Closest in time.
The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al · 2023
Closest in time.
Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan · 2023
Closest in time.
Chatgpt plugins
OpenAI · 2023
Closest in time.
An important next step on our ai journey
Sundar Pichai · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Closest in time.
Language models are multilingual chain-of-thought reasoners
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, et al · 2023
Closest in time.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2023
Closest in time.
ReAct: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2023
Closest in time.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2023
Closest in time.
Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola · 2023
Closest in time.
Webarena: A realistic web environment for building autonomous agents
Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, et al · 2023
Closest in time.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
Closest in time.
Chatgpt asks, blip-2 answers: Automatic questioning towards enriched visual descriptions, 2023
Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen, Wenxuan Zhang, and Mohamed Elhoseiny · 2023
Closest in time.