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Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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Visualbert: A simple and performant baseline for vision and language
Li, L. H.; Yatskar, M.; Yin, D.; Hsieh, C.-J.; and Chang, K.-W. 2019 · 1908
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Unifiedqa: Crossing format boundaries with a single qa system
Khashabi, D.; Min, S.; Khot, T.; Sabharwal, A.; Tafjord, O.; Clark, P.; and Hajishirzi, H. 2020 · 2005
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension
Kembhavi, A.; Seo, M.; Schwenk, D.; Choi, J.; Farhadi, A.; and Hajishirzi, H. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W.; Yogatama, D.; Dyer, C.; and Blunsom, P. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
Anderson, P.; He, X.; Buehler, C.; Teney, D.; Johnson, M.; Gould, S.; and Zhang, L. 2018 · 2018
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Jansen, P. A.; Wainwright, E.; Marmorstein, S.; and Morrison, C. T. 2018 · 2018
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Bilinear attention networks
Kim, J.-H.; Jun, J.; and Zhang, B.-T. 2018 · 2018
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Talmor, A.; Herzig, J.; Lourie, N.; and Berant, J. 2018 · 2018
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Dynamic fusion with intra-and inter-modality attention flow for visual question answering
Gao, P.; Jiang, Z.; You, H.; Lu, P.; Hoi, S. C.; Wang, X.; and Li, H. 2019 · 2019
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Deep modular co-attention networks for visual question answering
Yu, Z.; Yu, J.; Cui, Y.; Tao, D.; and Tian, Q. 2019 · 2019
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End-to-End Object Detection with Transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Earlier work this paper cites.
Big self-supervised models are strong semi-supervised learners
Chen, T.; Kornblith, S.; Swersky, K.; Norouzi, M.; and Hinton, G. E. 2020 · 2020
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Visuo-Lingustic Question Answering (VLQA) Challenge
Sampat, S. K.; Yang, Y.; and Baral, C. 2020 · 2020
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Training verifiers to solve math word problems
Cobbe, K.; Kosaraju, V.; Bavarian, M.; Chen, M.; Jun, H.; Kaiser, L.; Plappert, M.; Tworek, J.; Hilton, J.; Nakano, R.; et al. 2021 · 2021
Cited alongside, same era.
Explaining answers with entailment trees
Dalvi, B.; Jansen, P.; Tafjord, O.; Xie, Z.; Smith, H.; Pipatanangkura, L.; and Clark, P. 2021 · 2021
Cited alongside, same era.
Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva, M.; Khashabi, D.; Segal, E.; Khot, T.; Roth, D.; and Berant, J. 2021 · 2021
Cited alongside, same era.
Vilt: Vision-and-language transformer without convolution or region supervision
Kim, W.; Son, B.; and Kim, I. 2021 · 2021
Cited alongside, same era.
Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Lu, P.; Qiu, L.; Chen, J.; Xia, T.; Zhao, Y.; Zhang, W.; Yu, Z.; Liang, X.; and Zhu, S.-C. 2021 · 2021
Introducing chatgpt
OpenAI. 2022 · 2022
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Lamda: Language models for dialog applications
Thoppilan, R.; De Freitas, D.; Hall, J.; Shazeer, N.; Kulshreshtha, A.; Cheng, H.-T.; Jin, A.; Bos, T.; Baker, L.; Du, Y.; et al. 2022 · 2022
Later among the works it cites.
Iteratively prompt pre-trained language models for chain of thought
Wang, B.; Deng, X.; and Sun, H. 2022 · 2022
Later among the works it cites.
Automatic chain of thought prompting in large language models
Zhang, Z.; Zhang, A.; Li, M.; and Smola, A. 2022 · 2022
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Least-to-most prompting enables complex reasoning in large language models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Bousquet, O.; Le, Q.; and Chi, E. 2022 · 2022
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Cited alongside, same era.
Show your work: Scratchpads for intermediate computation with language models
Nye, M.; Andreassen, A. J.; Gur-Ari, G.; Michalewski, H.; Austin, J.; Bieber, D.; Dohan, D.; Lewkowycz, A.; Bosma, M.; Luan, D.; et al. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
Rubin, O.; Herzig, J.; and Berant, J. 2021 · 2021
Cited alongside, same era.
Chen, W.; Ma, X.; Wang, X.; and Cohen, W. W. 2022 · 2022
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Fu, Y.; Peng, H.; Sabharwal, A.; Clark, P.; and Khot, T. 2022 · 2022
Cited alongside, same era.
Large Language Models Are Reasoning Teachers
Ho, N.; Schmid, L.; and Yun, S.-Y. 2022 · 2022
Cited alongside, same era.
Large language models can self-improve
Huang, J.; Gu, S. S.; Hou, L.; Wu, Y.; Wang, X.; Yu, H.; and Han, J. 2022 · 2022
Cited alongside, same era.
Specializing Smaller Language Models towards Multi-Step Reasoning
Fu, Y.; Peng, H.; Ou, L.; Sabharwal, A.; and Khot, T. 2023 · 2023
Closest in time.
He, J.; Wang, L.; Hu, Y.; Liu, N.; Liu, H.; Xu, X.; and Shen, H. T. 2023 · 2023
Closest in time.
Hsieh, C.-Y.; Li, C.-L.; Yeh, C.-K.; Nakhost, H.; Fujii, Y.; Ratner, A.; Krishna, R.; Lee, C.-Y.; and Pfister, T. 2023 · 2023
Closest in time.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Hu, Z.; Lan, Y.; Wang, L.; Xu, W.; Lim, E.-P.; Lee, R. K.-W.; Bing, L.; and Poria, S. 2023 · 2023
Closest in time.
Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023
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Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Lu, P.; Peng, B.; Cheng, H.; Galley, M.; Chang, K.-W.; Wu, Y. N.; Zhu, S.-C.; and Gao, J. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
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Tian, Q.; Zhu, H.; Wang, L.; Li, Y.; and Lan, Y. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
Closest in time.
Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Wang, L.; Xu, W.; Lan, Y.; Hu, Z.; Lan, Y.; Lee, R. K.-W.; and Lim, E.-P. 2023 · 2023
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