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Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
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Deep reinforcement learning for dialogue generation
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Minimum risk training for neural machine translation
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Pytorch: An imperative style, high-performance deep learning library
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Language models are unsupervised multitask learners
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Language models are few-shot learners
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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You impress me: Dialogue generation via mutual persona perception
Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, and Dongmei Zhang. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Knowledge-grounded dialogue generation with pre-trained language models
Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, and Rui Yan. 2020 · 2020
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021 · 2021
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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, et al. 2021 · 2021
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
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Learning how to ask: Querying LMs with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2022 · 2022
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Prompt injection: Parameterization of fixed inputs
Eunbi Choi, Yongrae Jo, Joel Jang, and Minjoon Seo. 2022 · 2022
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RLPrompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022 · 2022
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
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Robustness of learning from task instructions
Jiasheng Gu, Hongyu Zhao, Hanzi Xu, Liangyu Nie, Hongyuan Mei, and Wenpeng Yin. 2022 · 2022
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Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2022 · 2022
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Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
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Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi. 2022 · 2022
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Fast model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. 2022 · 2022
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Gpt-4 technical report
OpenAI. 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, et al. 2022 · 2022
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Self-critiquing models for assisting human evaluators
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Toward human readable prompt tuning: Kubrick’s the shining is a good movie, and a good prompt too?
Weijia Shi, Xiaochuang Han, Hila Gonen, Ari Holtzman, Yulia Tsvetkov, and Luke Zettlemoyer. 2022 · 2022
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Defining and characterizing reward gaming
Joar Max Viktor Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger. 2022 · 2022
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Learning by distilling context
Charlie Snell, Dan Klein, and Ruiqi Zhong. 2022 · 2022
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
Meta Fundamental AI Research Diplomacy Team, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, et al. 2022 · 2022
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Solving math word problems with process-and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Cited alongside, same era.
DUnE: Dataset for unified editing
Afra Akyürek, Eric Pan, Garry Kuwanto, and Derry Wijaya. 2023 · 2023
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A general theoretical paradigm to understand learning from human preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos. 2023 · 2023
Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023 · 2023
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Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2023 · 2023
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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
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Sheared llama: Accelerating language model pre-training via structured pruning
Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, and Danqi Chen. 2023 · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
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Implicit chain of thought reasoning via knowledge distillation
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Rlcd: Reinforcement learning from contrast distillation for language model alignment
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RRHF: Rank responses to align language models with human feedback
Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. 2023 · 2023
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Slic-hf: Sequence likelihood calibration with human feedback
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Judging LLM-as-a-judge with MT-bench and chatbot arena
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