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Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language.
Language models are few-shot learners
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
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Meddialog: a large-scale medical dialogue dataset
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Don’t stop pretraining: Adapt language models to domains and tasks
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Automatic differentiation in pytorch
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Deep learning using rectified linear units (relu)
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
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Repairing without retraining: Avoiding disparate impact with counterfactual distributions
Hao Wang, Berk Ustun, and Flavio Calmon. 2019 · 2019
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Climbing towards NLU: On meaning, form, and understanding in the age of data
Emily M. Bender and Alexander Koller. 2020 · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 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 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi 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 Rush. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 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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Selecting better samples from pre-trained llms: A case study on question generation
Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Pauline Lucas, Hélène Sauzéon, and Pierre-Yves Oudeyer. 2022 · 2022
Later among the works it cites.
Using large language models to simulate multiple humans and replicate human subject studies
Gati V Aher, Rosa I Arriaga, and Adam Tauman Kalai. 2023 · 2023
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Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Niket Tandon, Aman Madaan, Peter Clark, and Yiming Yang. 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Towards teachable reasoning systems: Using a dynamic memory of user feedback for continual system improvement
Bhavana Dalvi, Oyvind Tafjord, and Peter Clark. 2022 · 2022
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Memory-assisted prompt editing to improve gpt-3 after deployment
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022 · 2022
Cited alongside, same era.
Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. 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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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022b
Cited in the paper.
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2023 · 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 · 2023
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Chain-of-thought prompting for responding to in-depth dialogue questions with llm
Hongru Wang, Rui Wang, Fei Mi, Zezhong Wang, Ruifeng Xu, and Kam-Fai Wong. 2023 · 2023
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Tidybot: Personalized robot assistance with large language models
Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, and Thomas Funkhouser. 2023 · 2023
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Baize: An open-source chat model with parameter-efficient tuning on self-chat data
Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley. 2023 · 2023
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Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge
Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, and Zhang You. 2023 · 2023
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Llama-adapter: Efficient fine-tuning of language models with zero-init attention
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. 2023 · 2023
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