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The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.
On information and sufficiency
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Rank analysis of incomplete block designs: I. the method of paired comparisons
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Optimal brain damage
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
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Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett · 2005
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Natural language processing (almost) from scratch
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Preference-based policy learning
Riad Akrour, Marc Schoenauer, and Michele Sebag · 2011
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Efficient estimation of word representations in vector space
Tomas Mikolov · 2013
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Rouge 2.0: Updated and improved measures for evaluation of summarization tasks
Kavita A. Ganesan · 2015
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Learning binary codes for maximum inner product search
Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, and Heng Tao Shen · 2015
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Tl; dr: Mining reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Frederick Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Tacotron: Towards end-to-end speech synthesis, 2017
Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous · 2017
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Improving language understanding by generative pre-training
Alec Radford · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Born again neural networks, 2018
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
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An introduction to domain adaptation and transfer learning
Wouter M Kouw and Marco Loog · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition, 2018
Pete Warden · 2018
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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M Lewis · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning, 2019
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, et al · 2019
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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Batch policy learning under constraints
Hoang Le, Cameron Voloshin, and Yisong Yue · 2019
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Off-policy evaluation via off-policy classification
Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, and Sergey Levine · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2019
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Language models are few-shot learners
Tom B Brown · 2020
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Fine-tuning language models from human preferences, 2020
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2020
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Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Human-centric dialog training via offline reinforcement learning, 2020
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, and Rosalind Picard · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig · 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
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Fresh: Interactive reward shaping in high-dimensional state spaces using human feedback
Baicen Xiao, Qifan Lu, Bhaskar Ramasubramanian, Andrew Clark, Linda Bushnell, and Radha Poovendran · 2020
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Quantifying differences in reward functions
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, and Jan Leike · 2020
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Dueling posterior sampling for preference-based reinforcement learning
Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, and Joel Burdick · 2020
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Preference-based reinforcement learning with finite-time guarantees
Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, and Artur Dubrawski · 2020
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Provably efficient reinforcement learning with linear function approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I Jordan · 2020
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Adapterfusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2020
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Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander M Rush, and Yoon Kim · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta · 2020
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Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
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Panns: Large-scale pretrained audio neural networks for audio pattern recognition
Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley · 2020
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Modifying memories in transformer models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar · 2020
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Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, and Artem Babenko · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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All you need is low (rank) defending against adversarial attacks on graphs
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis · 2020
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2021
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, et al · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Monte carlo tree search: a review of recent modifications and applications
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, and Jacek Ma’ndziuk · 2021
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Lora: Low-rank adaptation of large language models
J. Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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Jerry Zhi-Yang He and Anca D Dragan · 2021
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The empathic framework for task learning from implicit human feedback
Yuchen Cui, Qiping Zhang, Brad Knox, Alessandro Allievi, Peter Stone, and Scott Niekum · 2021
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Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation
Lin Guan, Mudit Verma, Suna Sihang Guo, Ruohan Zhang, and Subbarao Kambhampati · 2021
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Extractive opinion summarization in quantized transformer spaces
Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, and Mirella Lapata · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan · 2021
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Spot: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer · 2021
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Training neural networks with fixed sparse masks
Yi-Lin Sung, Varun Nair, and Colin A Raffel · 2021
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Composable sparse fine-tuning for cross-lingual transfer
Alan Ansell, Edoardo Maria Ponti, Anna Korhonen, and Ivan Vulić · 2021
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Raise a child in large language model: Towards effective and generalizable fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2021
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Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
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Unipelt: A unified framework for parameter-efficient language model tuning
Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen-tau Yih, and Madian Khabsa · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation, 2021
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C. H. Hoi · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2021
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Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning · 2021
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On cross-layer alignment for model fusion of heterogeneous neural networks
Dang Nguyen, Trang Thi Thu Nguyen, Khai Nguyen, D.Q. Phung, Hung Bui, and Nhat Ho · 2021
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Gan cocktail: mixing gans without dataset access
Omri Avrahami, Dani Lischinski, and Ohad Fried · 2021
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Personalized federated learning using hypernetworks
Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik · 2021
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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, et al · 2021
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Understanding dataset difficulty with v-usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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Paragraph-level rationale extraction through regularization: A case study on European court of human rights cases
Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos, and Prodromos Malakasiotis · 2021
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OpenAI Codex, 2021
OpenAI · 2021
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al · 2022
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Promptsource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al · 2022
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Training language models to follow instructions with human feedback
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 E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou · 2022
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick · 2022
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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
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Inducing structure in reward learning by learning features
Andreea Bobu, Marius Wiggert, Claire Tomlin, and Anca D Dragan · 2022
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Dynamics-aware comparison of learned reward functions
Blake Wulfe, Ashwin Balakrishna, Logan Ellis, Jean Mercat, Rowan McAllister, and Adrien Gaidon · 2022
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A general framework for reward function distances
Erik Jenner, Joar Max Viktor Skalse, and Adam Gleave · 2022
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Preprocessing reward functions for interpretability
Erik Jenner and Adam Gleave · 2022
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Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, and Liwei Wang · 2022
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Provably feedback-efficient reinforcement learning via active reward learning
Dingwen Kong and Lin Yang · 2022
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Generalized results for the existence and consistency of the mle in the bradley-terry-luce model, 2022
Heejong Bong and Alessandro Rinaldo · 2022
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi · 2022
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Rarr: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Gunho Park, Baeseong Park, Minsub Kim, Sungjae Lee, Jeonghoon Kim, Beomseok Kwon, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, and Dongsoo Lee · 2022
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Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, and Yuxiong He · 2022
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Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Language model compression with weighted low-rank factorization
Yen-Chang Hsu, Ting Hua, Sungen Chang, Qian Lou, Yilin Shen, and Hongxia Jin · 2022
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Xprompt: Exploring the extreme of prompt tuning
Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, and Dawei Song · 2022
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Idpg: An instance-dependent prompt generation method
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, VG Vydiswaran, and Hao Ma · 2022
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 2022
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Scaling & shifting your features: A new baseline for efficient model tuning
Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 2022
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Efficient fine-tuning of bert models on the edge
Danilo Vucetic, Mohammadreza Tayaranian, Maryam Ziaeefard, James J Clark, Brett H Meyer, and Warren J Gross · 2022
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Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi · 2022
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Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Explanations from large language models make small reasoners better
Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, et al · 2022
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Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn · 2022
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Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun · 2022
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Yukun Huang, Yanda Chen, Zhou Yu, and Kathleen McKeown · 2022
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Scaling instruction-finetuned language models, 2022
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei · 2022
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Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection
Ke Chen, Xingjian Du, Bilei Zhu, Zejun Ma, Taylor Berg-Kirkpatrick, and Shlomo Dubnov · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
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Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li · 2022
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Retrieval-augmented transformer for image captioning
Sara Sarto, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara · 2022
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
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Git re-basin: Merging models modulo permutation symmetries
Samuel K. Ainsworth, Jonathan Hayase, and Siddhartha S. Srinivasa · 2022
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Repair: Renormalizing permuted activations for interpolation repair
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, and Behnam Neyshabur · 2022
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Sum of ranked range loss for supervised learning
Shu Hu, Yiming Ying, Xin Wang, and Siwei Lyu · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp 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
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Crosslingual generalization through multitask finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, et al · 2022
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Legal prompt engineering for multilingual legal judgement prediction, 2022
Dietrich Trautmann, Alina Petrova, and Frank Schilder · 2022
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Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji · 2022
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A survey of large language models
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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
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
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Aligning language models with preferences through f-divergence minimization, 2023
Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, and Marc Dymetman · 2023
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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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Llama 2: Open foundation and fine-tuned chat models
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Gemini: a family of highly capable multimodal models
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Mods: Model-oriented data selection for instruction tuning
Qianlong Du, Chengqing Zong, and Jiajun Zhang · 2023
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Instruction mining: Instruction data selection for tuning large language models
Yihan Cao, Yanbin Kang, Chi Wang, and Lichao Sun · 2023
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Full parameter fine-tuning for large language models with limited resources
Kai Lv, Yuqing Yang, Tengxiao Liu, Qi jie Gao, Qipeng Guo, and Xipeng Qiu · 2023
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Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang · 2023
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Towards adaptive prefix tuning for parameter-efficient language model fine-tuning
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A survey of reinforcement learning from human feedback
Timo Kaufmann, Paul Weng, Viktor Bengs, and Eyke Hüllermeier · 2023
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Rlif: Interactive imitation learning as reinforcement learning
Jianlan Luo, Perry Dong, Yuexiang Zhai, Yi Ma, and Sergey Levine · 2023
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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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The effect of modeling human rationality level on learning rewards from multiple feedback types
Gaurav R Ghosal, Matthew Zurek, Daniel S Brown, and Anca D Dragan · 2023
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Sirl: similarity-based implicit representation learning
Andreea Bobu, Yi Liu, Rohin Shah, Daniel S Brown, and Anca D Dragan · 2023
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Starc: A general framework for quantifying differences between reward functions
Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani, Erik Jenner, Adam Gleave, and Alessandro Abate · 2023
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The trickle-down impact of reward (in-) consistency on rlhf
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Parl: A unified framework for policy alignment in reinforcement learning
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Principled reinforcement learning with human feedback from pairwise or k-wise comparisons
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Provable offline preference-based reinforcement learning
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Baldur: Whole-proof generation and repair with large language models
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Selfevolve: A code evolution framework via large language models
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Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, and Niket Tandon · 2023
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Self-edit: Fault-aware code editor for code generation
Kechi Zhang, Zhuo Li, Jia Li, Ge Li, and Zhi Jin · 2023
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Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston · 2023
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Flare: Fast learning of animatable and relightable mesh avatars
Shrisha Bharadwaj, Yufeng Zheng, Otmar Hilliges, Michael J. Black, and Victoria Fernandez-Abrevaya · 2023
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Shitong Duan, Xiaoyuan Yi, Peng Zhang, Tun Lu, Xing Xie, and Ning Gu · 2024
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Disentangling length from quality in direct preference optimization
Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn · 2024
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Direct nash optimization: Teaching language models to self-improve with general preferences, 2024
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Token-level direct preference optimization
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Negative preference optimization: From catastrophic collapse to effective unlearning
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Simpo: Simple preference optimization with a reference-free reward
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Iterative data smoothing: Mitigating reward overfitting and overoptimization in rlhf
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sdpo: Don’t use your data all at once, 2024
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Random contrastive interaction for particle swarm optimization in high-dimensional environment
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Self-refinement of language models from external proxy metrics feedback
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Prefer: Prompt ensemble learning via feedback-reflect-refine
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Cycle: Learning to self-refine the code generation
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Self-refine instruction-tuning for aligning reasoning in language models
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Verification and refinement of natural language explanations through llm-symbolic theorem proving
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Enable lanuguage models to implicitly learn self-improvement from data
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The unreasonable effectiveness of easy training data for hard tasks
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Hotaq: Hardware oriented token adaptive quantization for large language models
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Adversarial moment-matching distillation of large language models, 2024
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Qvq: To see the world with wisdom
QWen · 2024
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Voicecraft: Zero-shot speech editing and text-to-speech in the wild
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