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The rapid growth of Large Language Models (LLMs) usage has highlighted the importance of gradient-free in-context learning (ICL).
Language Models Are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 1901
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Latent Gaussian Model Boosting
Sigrist, F. 2023 · 1905
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Okapi at TREC-4
Robertson, SE.; Walker, S.; Beaulieu, MM.; Gatford, M.; and Payne, A. 1996 · 1996
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Dimensionality Reduction by Learning an Invariant Mapping
Hadsell, R.; Chopra, S.; and LeCun, Y. 2006 · 2006
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For Objective Causal Inference, Design Trumps Analysis
Rubin, D. B. 2008 · 2008
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A General and Simple Method for Obtaining R 2 from Generalized Linear Mixed-effects Models
Nakagawa, S.; and Schielzeth, H. 2013 · 2013
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Mixed-Effects Random Forest for Clustered Data
Hajjem, A.; Bellavance, F.; and Larocque, D. 2014 · 2014
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Making the v in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y.; Khot, T.; Summers-Stay, D.; Batra, D.; and Parikh, D. 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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VizWiz Grand Challenge: Answering Visual Questions from Blind People
Gurari, D.; Li, Q.; Stangl, A. J.; Guo, A.; Lin, C.; Grauman, K.; Luo, J.; and Bigham, J. P. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Hudson, D. A.; and Manning, C. D. 2019 · 2019
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DECOUPLED WEIGHT DECAY REGULARIZATION
Loshchilov, I.; and Hutter, F. 2019 · 2019
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Towards VQA Models That Can Read
Singh, A.; Natarajan, V.; Shah, M.; Jiang, Y.; Chen, X.; Batra, D.; Parikh, D.; and Rohrbach, M. 2019 · 2019
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An Introduction to Mixed Models for Experimental Psychology
Singmann, H.; and Kellen, D. 2019 · 2019
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Exploring Hate Speech Detection in Multimodal Publications
Gomez, R.; Gibert, J.; Gomez, L.; and Karatzas, D. 2020 · 2020
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Supervised Contrastive Learning
Khosla, P.; Tian, Y.; Teterwak, P.; Wang, C.; Isola, P.; Maschinot, A.; Krishnan, D.; and Sarna, A. 2020 · 2020
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The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
Kiela, D.; Firooz, H.; Mohan, A.; Goswami, V.; Singh, A.; Ringshia, P.; and Testuggine, D. 2020 · 2020
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Contrastive Representation Learning: A Framework and Review
Le-Khac, P. H.; Healy, G.; and Smeaton, A. F. 2020 · 2020
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Impact of Literacy on the Functional Connectivity of Vision and Language Related Networks
López-Barroso, D.; Thiebaut De Schotten, M.; Morais, J.; Kolinsky, R.; Braga, L. W.; Guerreiro-Tauil, A.; Dehaene, S.; and Cohen, L. 2020 · 2020
Earlier work this paper cites.
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.; Krueger, G.; and Sutskever, I. 2021 · 2021
Cited alongside, same era.
Trustworthy Artificial Intelligence
Thiebes, S.; Lins, S.; and Sunyaev, A. 2021 · 2021
Cited alongside, same era.
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities
Zhao, J.; Li, R.; and Jin, Q. 2021 · 2021
Cited alongside, same era.
Unifying Grokking and Double Descent
Davies, X.; Langosco, L.; and Krueger, D. 2022 · 2022
Cited alongside, same era.
On Explaining Multimodal Hateful Meme Detection Models
Hee, M. S.; Lee, R. K.-W.; and Chong, W.-H. 2022 · 2022
Cited alongside, same era.
What Makes Good In-Context Examples for GPT-3?
Liu, J.; Shen, D.; Zhang, Y.; Dolan, B.; Carin, L.; and Chen, W. 2022 · 2022
The Linear Representation Hypothesis and the Geometry of Large Language Models
Park, K.; Choe, Y. J.; and Veitch, V. 2023 · 2023
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On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning
Qu, Y.; He, X.; Pierson, S.; Backes, M.; Zhang, Y.; and Zannettou, S. 2023 · 2023
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In-Context Learning with Transformer Is Really Equivalent to a Contrastive Learning Pattern
Ren, R.; and Liu, Y. 2023 · 2023
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CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
Tang, Z.; Yang, Z.; Khademi, M.; Liu, Y.; Zhu, C.; and Bansal, M. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; and Stone, K. 2023 · 2023
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Cited alongside, same era.
An Explanation of In-context Learning as Implicit Bayesian Inference
Xie, S. M.; Raghunathan, A.; Liang, P.; and Ma, T. 2022 · 2022
Cited alongside, same era.
Sequential Modeling Enables Scalable Learning for Large Vision Models
Bai, Y.; Geng, X.; Mangalam, K.; Bar, A.; Yuille, A.; Darrell, T.; Malik, J.; and Efros, A. A. 2023 · 2023
Cited alongside, same era.
Bianchi, F.; Suzgun, M.; Attanasio, G.; Röttger, P.; Jurafsky, D.; Hashimoto, T.; and Zou, J. 2023 · 2023
Cited alongside, same era.
Generative AI and Prompt Engineering: The Art of Whispering to Let the Genie Out of the Algorithmic World
Bozkurt, A.; and Sharma, R. C. 2023 · 2023
Cited alongside, same era.
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models
Bulat, A.; and Tzimiropoulos, G. 2023 · 2023
Cited alongside, same era.
Meta-in-Context Learning in Large Language Models
Coda-Forno, J.; Binz, M.; Akata, Z.; Botvinick, M.; Wang, J. X.; and Schulz, E. 2023 · 2023
Cited alongside, same era.
Transformers Learn In-Context by Gradient Descent
von Oswald, J.; Niklasson, E.; Randazzo, Ettore; Sacramento, Jo\~{a}o; Mordvintsev, Alexander; Zhmoginov, Andrey; and Vladymyrov, Max. 2023 · 2023
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Multi-Modal Learning with Missing Modality via Shared-Specific Feature Modelling
Wang, H.; Chen, Y.; Ma, C.; Avery, J.; Hull, L.; and Carneiro, G. 2023a · 2023
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NExT-GPT: Any-to-Any Multimodal LLM
Wu, S.; Fei, H.; Qu, L.; Ji, W.; and Chua, T.-S. 2023 · 2023
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MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
Yu, W.; Yang, Z.; Li, L.; Wang, J.; Lin, K.; Liu, Z.; Wang, X.; and Wang, L. 2023 · 2023
Later among the works it cites.
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
Zheng, K.; He, X.; and Wang, X. E. 2023 · 2023
Later among the works it cites.
Jump to Conclusions: Short-Cutting Transformers with Linear Transformations
Din, A. Y.; Karidi, T.; Choshen, L.; and Geva, M. 2024 · 2024
Closest in time.
Not All Layers of LLMs Are Necessary During Inference
Fan, S.; Jiang, X.; Li, X.; Meng, X.; Han, P.; Shang, S.; Sun, A.; Wang, Y.; and Wang, Z. 2024 · 2024
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BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
Hu, W.; Xu, Y.; Li, Y.; Li, W.; Chen, Z.; and Tu, Z. 2024 · 2024
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Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: A Case Study on Hateful Memes
Miyanishi, Y.; and Nguyen, M. L. 2024 · 2024
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Transformers Are Multi-State RNNs
Oren, M.; Hassid, M.; Adi, Y.; and Schwartz, R. 2024 · 2024
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Language Models Are Better Than Humans at Next-token Prediction
Shlegeris, B.; Roger, F.; Chan, L.; and McLean, E. 2024 · 2024
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Learning to Retrieve In-Context Examples for Large Language Models
Wang, L.; Yang, N.; and Wei, F. 2024 · 2024
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MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Yue, X.; Ni, Y.; Zhang, K.; Zheng, T.; Liu, R.; Zhang, G.; Stevens, S.; Jiang, D.; Ren, W.; Sun, Y.; Wei, C.; Yu, B.; Yuan, R.; Sun, R.; Yin, M.; Zheng, B.; Yang, Z.; Liu, Y.; Huang, W.; Sun, H.; Su, Y.; and Chen, W. 2024 · 2024
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MM-LLMs: Recent Advances in MultiModal Large Language Models
Zhang, D.; Yu, Y.; Li, C.; Dong, J.; Su, D.; Chu, C.; and Yu, D. 2024 · 2024
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