Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have led to a surge in collaborative writing with model assistance.
A universal algorithm for sequential data compression
Jacob Ziv and Abraham Lempel. 1977 · 1977
Earlier work this paper cites.
Compression of individual sequences via variable-rate coding
Jacob Ziv and Abraham Lempel. 1978 · 1978
Earlier work this paper cites.
BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2004
Earlier work this paper cites.
Say anything: A massively collaborative open domain story writing companion
Reid Swanson and Andrew S Gordon. 2008 · 2008
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Say anything: Using textual case-based reasoning to enable open-domain interactive storytelling
Reid Swanson and Andrew S Gordon. 2012 · 2012
Earlier work this paper cites.
Poetry of the crowd: A human computation algorithm to convert prose into rhyming verse
Quanze Chen, Chenyang Lei, Wei Xu, Ellie Pavlick, and Chris Callison-Burch. 2014 · 2014
Earlier work this paper cites.
Creative help: A story writing assistant
Melissa Roemmele and Andrew S Gordon. 2015 · 2015
Earlier work this paper cites.
Hafez: an interactive poetry generation system
Marjan Ghazvininejad, Xing Shi, Jay Priyadarshi, and Kevin Knight. 2017 · 2017
Earlier work this paper cites.
Evaluating story generation systems using automated linguistic analyses
Melissa Roemmele, Andrew S Gordon, and Reid Swanson. 2017 · 2017
Earlier work this paper cites.
Creative writing with a machine in the loop: Case studies on slogans and stories
Elizabeth Clark, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A Smith. 2018 · 2018
Earlier work this paper cites.
Automated assistance for creative writing with an rnn language model
Melissa Roemmele and Andrew S Gordon. 2018 · 2018
Earlier work this paper cites.
Texygen: A benchmarking platform for text generation models
Y. Zhu, S. Lu, L. Zheng, J. Guo, W. Zhang, J. Wang, and Y. Yu. 2018 · 2018
Earlier work this paper cites.
Metaphoria: An algorithmic companion for metaphor creation
Katy Ilonka Gero and Lydia B Chilton. 2019 · 2019
Earlier work this paper cites.
Do massively pretrained language models make better storytellers?
Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, and Christopher D. Manning. 2019 · 2019
Earlier work this paper cites.
How to compare summarizers without target length? pitfalls, solutions and re-examination of the neural summarization literature
Simeng Sun, Ori Shapira, Ido Dagan, and Ani Nenkova. 2019 · 2019
Earlier work this paper cites.
Storium: A dataset and evaluation platform for machine-in-the-loop story generation
Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer. 2020 · 2020
Earlier work this paper cites.
Predictive text encourages predictable writing
Kenneth C Arnold, Krysta Chauncey, and Krzysztof Z Gajos. 2020 · 2020
Cited alongside, same era.
Ai-mediated communication: Definition, research agenda, and ethical considerations
Jeffrey T Hancock, Mor Naaman, and Karen Levy. 2020 · 2020
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
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, Dorottya 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, and Percy Liang. 2021 · 2021
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. 2022 · 2022
Later among the works it cites.
Machine-in-the-loop rewriting for creative image captioning
Vishakh Padmakumar and He He. 2022 · 2022
Later among the works it cites.
Wordcraft: Story writing with large language models
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022 · 2022
Later among the works it cites.
Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing
Advait Bhat, Saaket Agashe, Parth Oberoi, Niharika Mohile, Ravi Jangir, and Anirudha Joshi. 2023 · 2023
Closest in time.
Open problems and fundamental limitations of reinforcement learning from human feedback
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The impact of multiple parallel phrase suggestions on email input and composition behaviour of native and non-native english writers
Daniel Buschek, Martin Zürn, and Malin Eiband. 2021 · 2021
Cited alongside, same era.
Algorithmic monoculture and social welfare
J. Kleinberg and M. Raghavan. 2021 · 2021
Cited alongside, same era.
Divergence frontiers for generative models: Sample complexity, quantization effects, and frontier integrals
Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Cited alongside, same era.
Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Cited alongside, same era.
Story centaur: Large language model few shot learning as a creative writing tool
Ben Swanson, Kory Mathewson, Ben Pietrzak, Sherol Chen, and Monica Dinalescu. 2021 · 2021
Cited alongside, same era.
Evaluating the evaluation of diversity in natural language generation
Guy Tevet and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
Cited alongside, same era.
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. 2023 · 2023
Closest in time.
Towards measuring the representation of subjective global opinions in language models
E. Durmus, K. Nyugen, T. I. Liao, N. Schiefer, A. Askell, A. Bakhtin, C. Chen, Z. Hatfield-Dodds, D. Hernandez, N. Joseph, L. Lovitt, S. McCandlish, O. Sikder, A. Tamkin, J. Thamkul, J. Kaplan, J. Clark, and D. Ganguli. 2023 · 2023
Closest in time.
Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
Sebastian Gehrmann, Elizabeth Clark, and Thibault Sellam. 2023 · 2023
Closest in time.
Co-writing with opinionated language models affects users’ views
Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman. 2023 · 2023
Closest in time.
Contrastive decoding: Open-ended text generation as optimization
X. L. Li, A. Holtzman, D. Fried, P. Liang, J. Eisner, T. Hashimoto, L. Zettlemoyer, and M. Lewis. 2023 · 2023
Closest in time.
Rethinking model evaluation as narrowing the socio-technical gap
Q Vera Liao and Ziang Xiao. 2023 · 2023
Closest in time.
Locally typical sampling
C. Meister, T. Pimentel, G. Wiher, and R. Cotterell. 2023 · 2023
Closest in time.
Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals
Piotr Mirowski, Kory W Mathewson, Jaylen Pittman, and Richard Evans. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Mauve scores for generative models: Theory and practice
Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, and Zaid Harchaoui. 2023 · 2023
Closest in time.
Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
Closest in time.
Evaluating the social impact of generative ai systems in systems and society
Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, et al. 2023 · 2023
Closest in time.
Reward collapse in aligning large language models
Z. Song, T. Cai, J. D. Lee, and W. J. Su. 2023 · 2023
Closest in time.
Data feedback loops: Model-driven amplification of dataset biases
R. Taori and T. B. Hashimoto. 2023 · 2023
Closest in time.