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While visual question-answering (VQA) benchmarks have catalyzed the development of reasoning techniques, they have focused on vertical thinking.
Language Models are Few-Shot Learners
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MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
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Vertical versus lateral thinking
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Rebus puzzles as insight problems
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Finding Similar Items , 68–122
Leskovec, J.; Rajaraman, A.; and Ullman, J. D. 2014 · 2014
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A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Malinowski, M.; and Fritz, M. 2015 · 2015
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VQA: Visual Question Answering
Agrawal, A.; Lu, J.; Antol, S.; Mitchell, M.; Zitnick, C. L.; Batra, D.; and Parikh, D. 2016 · 2016
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De Bono, E. 2016 · 2016
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Validation of Italian rebus puzzles and compound remote associate problems
Salvi, C.; Costantini, G.; Bricolo, E.; Perugini, M.; and Beeman, M. 2016 · 2016
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Visual7W: Grounded Question Answering in Images
Zhu, Y.; Groth, O.; Bernstein, M.; and Fei-Fei, L. 2016 · 2016
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The History of the New World: Benzoni’s Historia del Mondo Nuovo
Benzoni, G. 2017 · 2017
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CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J.; Hariharan, B.; van der Maaten, L.; Fei-Fei, L.; Lawrence Zitnick, C.; and Girshick, R. 2017 · 2017
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Measuring abstract reasoning in neural networks
Barrett, D. G. T.; Hill, F.; Santoro, A.; Morcos, A. S.; and Lillicrap, T. 2018 · 2018
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Normative Data for 84 UK English Rebus Puzzles
Threadgold, E.; Marsh, J. E.; and Ball, L. J. 2018 · 2018
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LADEC: The Large Database of English Compounds
Gagné, C. L.; Spalding, T. L.; and Schmidtke, D. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
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RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature
de Faria, A. C. A. M.; de Castro Bastos, F.; da Silva, J. V. N. A.; Fabris, V. L.; de Sousa Uchoa, V.; de Aguiar Neto, D. G.; and dos Santos, C. F. G. 2023 · 2023
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Gemini: A Family of Highly Capable Multimodal Models
DeepMind. 2023 · 2023
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BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Jiang, Y.; Ilievski, F.; Ma, K.; and Sourati, Z. 2023b · 2023
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Language Models with Rationality
Kassner, N.; Tafjord, O.; Sabharwal, A.; Richardson, K.; Schütze, H.; and Clark, P. 2023 · 2023
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Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Lu, P.; Bansal, H.; Xia, T.; Liu, J.; Li, C.; Hajishirzi, H.; Cheng, H.; Chang, K.-W.; Galley, M.; and Gao, J. 2023 · 2023
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BONGARD-LOGO: a new benchmark for human-level concept learning and reasoning
Nie, W.; Yu, Z.; Mao, L.; Patel, A. B.; Zhu, Y.; and Anandkumar, A. 2020 · 2020
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Learning representations that support extrapolation
Webb, T. W.; Dulberg, Z.; Frankland, S. M.; Petrov, A. A.; O’Reilly, R. C.; and Cohen, J. D. 2020 · 2020
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Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning
Zhang, W.; Zhang, C.; Zhu, Y.; and Zhu, S. 2020 · 2020
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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
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Scaling Instruction-Finetuned Language Models
Chung, H. W.; et al. 2022 · 2022
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QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
Li, Z.; and Søgaard, A. 2022 · 2022
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A review of emerging research directions in Abstract Visual Reasoning
Małkiński, M.; and Mańdziuk, J. 2023 · 2023
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CogVLM: Visual Expert for Pretrained Language Models
Wang, W.; Lv, Q.; Yu, W.; Hong, W.; Qi, J.; Wang, Y.; Ji, J.; Yang, Z.; Zhao, L.; Song, X.; Xu, J.; Xu, B.; Li, J.; Dong, Y.; Ding, M.; and Tang, J. 2023 · 2023
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Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Zheng, L.; Chiang, W.-L.; Sheng, Y.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Lin, Z.; Li, Z.; Li, D.; Xing, E. P.; Zhang, H.; Gonzalez, J. E.; and Stoica, I. 2023 · 2023
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Fuyu-8B: A Multimodal Architecture for AI Agents
Bavishi, R.; Elsen, E.; Hawthorne, C.; Nye, M.; Odena, A.; Somani, A.; and Taşırlar, S. 2023 · 2024
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REBUS: A Robust Evaluation Benchmark of Understanding Symbols
Gritsevskiy, A.; Panickssery, A.; Kirtland, A.; Kauffman, D.; Gundlach, H.; Gritsevskaya, I.; Cavanagh, J.; Chiang, J.; Roux, L. L.; and Hung, M. 2024 · 2024
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LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles
Huang, S.; Ma, S.; Li, Y.; Huang, M.; Zou, W.; Zhang, W.; and Zheng, H. 2024 · 2024
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Semeval-2024 task 9: Brainteaser: A novel task defying common sense
Jiang, Y.; Ilievski, F.; and Ma, K. 2024 · 2024
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MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
Jiang, Y.; Zhang, J.; Sun, K.; Sourati, Z.; Ahrabian, K.; Ma, K.; Ilievski, F.; and Pujara, J. 2024 · 2024
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OpenAI. 2024 · 2024
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Wang, W.; Fang, T.; Li, C.; Shi, H.; Ding, W.; Xu, B.; Wang, Z.; Bai, J.; Liu, X.; Cheng, J.; et al. 2024 · 2024
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Exploring perceptual limitation of multimodal large language models
Zhang, J.; Hu, J.; Khayatkhoei, M.; Ilievski, F.; and Sun, M. 2024 · 2024
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