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While large language models (LLMs) excel in various natural language processing tasks, their huge size and the inaccessibility of parameters present challenges for practical deployment.
Language models are few-shot learners
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Language models are open knowledge graphs
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Neurosymbolic ai: The 3rd wave
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Solving general arithmetic word problems
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Data-free knowledge distillation for deep neural networks
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Zero-shot knowledge distillation in deep networks
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Language models as knowledge bases?
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
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Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
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Self-distillation amplifies regularization in hilbert space
Hossein Mobahi, Mehrdad Farajtabar, and Peter Bartlett. 2020 · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Data-free knowledge transfer: A survey
Yuang Liu, Wei Zhang, Jun Wang, and Jianyong Wang. 2021 · 2021
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. 2021 · 2021
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Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks
Lin Wang and Kuk-Jin Yoon. 2021 · 2021
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CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
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See through gradients: Image batch recovery via gradinversion
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022 · 2022
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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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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Knife: Distilling meta-reasoning knowledge with free-text rationales
Aaron Chan, Zhiyuan Zeng, Wyatt Lake, Brihi Joshi, Hanjie Chen, and Xiang Ren · 2023
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Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Mathematical capabilities of chatgpt
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Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
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ROSCOE: A suite of metrics for scoring step-by-step reasoning
Olga Golovneva, Moya Peng Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz. 2023 · 2023
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
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Bert on a data diet: Finding important examples by gradient-based pruning
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
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Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 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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On the advance of making language models better reasoners
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
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Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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Measuring faithfulness in chain-of-thought reasoning
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Deductive verification of chain-of-thought reasoning
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Self-refine: Iterative refinement with self-feedback
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Art: Automatic multi-step reasoning and tool-use for large language models
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Vipergpt: Visual inference via python execution for reasoning
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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, et al. 2023 · 2023
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Scott: Self-consistent chain-of-thought distillation
Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, and Xiang Ren. 2023 · 2023
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Decomposition enhances reasoning via self-evaluation guided decoding
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Knowledge-augmented reasoning distillation for small language models in knowledge-intensive tasks
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Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting
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Tree of thoughts: Deliberate problem solving with large language models
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Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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