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Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots.
Bounding the capabilities of large language models in open text generation with prompt constraints
Albert Lu, Hongxin Zhang, Yanzhe Zhang, Xuezhi Wang, and Diyi Yang. 2023 · 2008
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Towards controllable story generation
Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018 · 2018
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From plots to endings: A reinforced pointer generator for story ending generation
Yan Zhao, Lu Liu, Chunhua Liu, Ruoyao Yang, and Dong Yu. 2018 · 2018
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Improving neural story generation by targeted common sense grounding
Huanru Henry Mao, Bodhisattwa Prasad Majumder, Julian McAuley, and Garrison Cottrell. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Controllable neural story plot generation via reward shaping
Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, and Mark O. Riedl. 2019 · 2019
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Enhancing pre-trained language representations with rich knowledge for machine reading comprehension
An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, and Sujian Li. 2019 · 2019
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Automated storytelling via causal, commonsense plot ordering
Prithviraj Ammanabrolu, Wesley Cheung, William Broniec, and Mark O. Riedl. 2020 · 2020
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Language models are few-shot learners
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 · 2020
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A knowledge-enhanced pretraining model for commonsense story generation
Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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PlotMachines: Outline-conditioned generation with dynamic plot state tracking
Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020 · 2020
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020 · 2020
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Self-attention and retrieval enhanced neural networks for essay generation
Wei Wang, Hai-Tao Zheng, and Zibo Lin. 2020b · 2020
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Automatic story generation: Challenges and attempts
Amal Alabdulkarim, Siyan Li, and Xiangyu Peng. 2021 · 2021
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Automatic story generation: A survey of approaches
Arwa I. Alhussain and Aqil M. Azmi. 2021 · 2021
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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The perils of using Mechanical Turk to evaluate open-ended text generation
Marzena Karpinska, Nader Akoury, and Mohit Iyyer. 2021 · 2021
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Few-shot text generation with natural language instructions
Timo Schick and Hinrich Schütze. 2021 · 2021
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GPT3Mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyoung Park. 2021 · 2021
How good are gpt models at machine translation? a comprehensive evaluation
Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, and Hany Hassan Awadalla. 2023 · 2023
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Large language models humanize technology
Pratyush Kumar. 2023 · 2023
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Language modeling with latent situations
Belinda Z. Li, Maxwell Nye, and Jacob Andreas. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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OpenAI. 2023 · 2023
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Efficient (soft) Q-learning for text generation with limited good data
Han Guo, Bowen Tan, Zhengzhong Liu, Eric Xing, and Zhiting Hu. 2022 · 2022
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ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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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, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Inferring the reader: Guiding automated story generation with commonsense reasoning
Xiangyu Peng, Siyan Li, Sarah Wiegreffe, and Mark Riedl. 2022 · 2022
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Retrieval, selection and writing: A three-stage knowledge grounded storytelling model
Wentao Qin and Dongyan Zhao. 2022 · 2022
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Data augmentation for neural nlp
Domagoj Pluščec and Jan Šnajder. 2023 · 2023
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Shouvon Sarker, Lijun Qian, and Xishuang Dong. 2023 · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
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Open-world story generation with structured knowledge enhancement: A comprehensive survey
Yuxin Wang, Jieru Lin, Zhiwei Yu, Wei Hu, and Börje F. Karlsson. 2023 · 2023
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
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Can very large pretrained language models learn storytelling with a few examples?
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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Can large language models be an alternative to human evaluation?
Wanyue Zhai, Jonathan Rusert, Zubair Shafiq, and Padmini Srinivasan. 2023 · 2023
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Ceg: A joint model for causal commonsense events enhanced story ending generation
Yushi Zhang, Yan Yang, Ming Gu, Feng Gao, Chengcai Chen, and Liang He. 2023 · 2023
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