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Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI.
Minds, brains, and programs
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ROUGE: A package for automatic evaluation of summaries
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Belief rule-base inference methodology using the evidential reasoning approach-rimer
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Artificial general intelligence
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Abduction
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Im2text: Describing images using 1 million captioned photographs
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Dual-process theories of higher cognition: Advancing the debate
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Analogy and analogical reasoning
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Referitgame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
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Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2015
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Cider: Consensus-based image description evaluation, 2015
Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh · 2015
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MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi · 2016
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Generation and comprehension of unambiguous object descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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A concise introduction to logic
Craig DeLancey · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Logical reasoning
Bradley H Dowden · 2018
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Babyai: A platform to study the sample efficiency of grounded language learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Dvqa: Understanding data visualizations via question answering
Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan · 2018
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Social iqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi · 2019
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Textworld: A learning environment for text-based games
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Nocaps: Novel object captioning at scale
Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson · 2019
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Raven: A dataset for relational and analogical visual reasoning
Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, and Song-Chun Zhu · 2019
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Lvis: A dataset for large vocabulary instance segmentation, 2019
Agrim Gupta, Piotr Dollár, and Ross Girshick · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Ocr-vqa: Visual question answering by reading text in images
Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty · 2019
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Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al · 2019
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Dsfd: dual shot face detector
Jian Li, Yabiao Wang, Changan Wang, Ying Tai, Jianjun Qian, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang · 2019
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Logical reasoning in formal and everyday reasoning tasks
Hugo Bronkhorst, Gerrit Roorda, Cor Suhre, and Martin Goedhart · 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
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Commongen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren · 2020
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Alfworld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Textcaps: a dataset for image captioning with reading comprehension
Oleksii Sidorov, Ronghang Hu, Marcus Rohrbach, and Amanpreet Singh · 2020
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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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
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Proofwriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar · 2021
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Ai2d-rst: A multimodal corpus of 1000 primary school science diagrams
Tuomo Hiippala, Malihe Alikhani, Jonas Haverinen, Timo Kalliokoski, Evanfiya Logacheva, Serafina Orekhova, Aino Tuomainen, Matthew Stone, and John A Bateman · 2021
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Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alex Schwing, and Alexander Kirillov · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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Vx2text: End-to-end learning of video-based text generation from multimodal inputs
Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, Devi Parikh, and Lorenzo Torresani · 2021
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Scienceqa: A novel resource for question answering on scholarly articles
Tanik Saikh, Tirthankar Ghosal, Amish Mittal, Asif Ekbal, and Pushpak Bhattacharyya · 2022
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Flamingo: a visual language model for few-shot learning, 2022
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 2022
Towards end-to-end embodied decision making via multi-modal large language model: Explorations with gpt4-vision and beyond
Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao, Zefan Cai, Yuchi Wang, Tianyu Liu, and Baobao Chang · 2023
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M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models
Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing · 2023
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Mmbench: Is your multi-modal model an all-around player?, 2023
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, and Dahua Lin · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al · 2023
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Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark, 2023
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, et al · 2022
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He · 2022
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On the paradox of learning to reason from data
Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, and Guy Van den Broeck · 2022
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Folio: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, et al · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al · 2022
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Webshop: Towards scalable real-world web interaction with grounded language agents
Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan · 2022
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Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, Jing Shao, and Wanli Ouyang · 2023
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Halle-switch: Controlling object hallucination in large vision language models, 2023
Bohan Zhai, Shijia Yang, Chenfeng Xu, Sheng Shen, Kurt Keutzer, and Manling Li · 2023
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Visit-bench: A benchmark for vision-language instruction following inspired by real-world use, 2023
Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, and Ludwig Schimdt · 2023
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Seed-bench: Benchmarking multimodal llms with generative comprehension, 2023
Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan · 2023
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Tiny lvlm-ehub: Early multimodal experiments with bard, 2023
Wenqi Shao, Yutao Hu, Peng Gao, Meng Lei, Kaipeng Zhang, Fanqing Meng, Peng Xu, Siyuan Huang, Hongsheng Li, Yu Qiao, and Ping Luo · 2023
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What matters in training a gpt4-style language model with multimodal inputs?, 2023
Yan Zeng, Hanbo Zhang, Jiani Zheng, Jiangnan Xia, Guoqiang Wei, Yang Wei, Yuchen Zhang, and Tao Kong · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Fool your (vision and) language model with embarrassingly simple permutations, 2023
Yongshuo Zong, Tingyang Yu, Bingchen Zhao, Ruchika Chavhan, and Timothy Hospedales · 2023
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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 · 2023
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React: Synergizing reasoning and acting in language models, 2023
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2023
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Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Bloom: A 176b-parameter open-access multilingual language model, 2023
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al · 2023
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Llama: Open and efficient foundation language models, 2023
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
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
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Improved baselines with visual instruction tuning
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Otter: A multi-modal model with in-context instruction tuning, 2023
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Cogvlm: Visual expert for pretrained language models, 2023
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Multimodal c4: An open, billion-scale corpus of images interleaved with text
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Datacomp: In search of the next generation of multimodal datasets
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Slimorca: An open dataset of gpt-4 augmented flan reasoning traces, with verification, 2023
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Llavar: Enhanced visual instruction tuning for text-rich image understanding
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Mitigating hallucination in large multi-modal models via robust instruction tuning
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Openflamingo: An open-source framework for training large autoregressive vision-language models
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Introducing idefics: An open reproduction of state-of-the-art visual language model, August 2023
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Mimic-it: Multi-modal in-context instruction tuning, 2023
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang, Chunyuan Li, and Ziwei Liu · 2023
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Mmicl: Empowering vision-language model with multi-modal in-context learning, 2023
Haozhe Zhao, Zefan Cai, Shuzheng Si, Xiaojian Ma, Kaikai An, Liang Chen, Zixuan Liu, Sheng Wang, Wenjuan Han, and Baobao Chang · 2023
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Grounding language models to images for multimodal inputs and outputs
Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried · 2023
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Self-adaptive in-context learning: An information compression perspective for in-context example selection and ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong · 2023
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From images to textual prompts: Zero-shot visual question answering with frozen large language models
Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, and Steven Hoi · 2023
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Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Dhruv Shah, Błażej Osiński, Sergey Levine, et al · 2023
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Collaborating with language models for embodied reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, and Rob Fergus · 2023
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Statler: State-maintaining language models for embodied reasoning
Takuma Yoneda, Jiading Fang, Peng Li, Huanyu Zhang, Tianchong Jiang, Shengjie Lin, Ben Picker, David Yunis, Hongyuan Mei, and Matthew R Walter · 2023
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Inner monologue: Embodied reasoning through planning with language models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al · 2023
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2023
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Distilling internet-scale vision-language models into embodied agents
Theodore Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, and Ishita Dasgupta · 2023
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Toolformer: Language models can teach themselves to use tools, 2023
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Art: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
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Tool learning with foundation models, 2023
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Augmented language models: a survey, 2023
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Foundation models for decision making: Problems, methods, and opportunities, 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis, 2023
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Tool documentation enables zero-shot tool-usage with large language models, 2023
Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister · 2023
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Tree of thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez · 2023
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Visual programming: Compositional visual reasoning without training
Tanmay Gupta and Aniruddha Kembhavi · 2023
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Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang · 2023
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Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Llama-adapter v2: Parameter-efficient visual instruction model, 2023
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, and Yu Qiao · 2023
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mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration, 2023
Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou · 2023
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Gemini: a family of highly capable multimodal models
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Gpt-4v(ision) technical work and authors., 2023
OpenAI · 2023
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Qwen-VL Team, 2023
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Introducing our multimodal models, 2023
Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sağnak Taşırlar · 2023
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Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition, 2023
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Obelisc: An open web-scale filtered dataset of interleaved image-text documents
Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M Rush, Douwe Kiela, et al · 2023
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Generative pretraining in multimodality
Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang · 2023
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Towards fast adaptation of pretrained contrastive models for multi-channel video-language retrieval
Xudong Lin, Simran Tiwari, Shiyuan Huang, Manling Li, Mike Zheng Shou, Heng Ji, and Shih-Fu Chang · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
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Aligning large multimodal models with factually augmented rlhf
Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al · 2023
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Infimm:, 2024
InfiMM Team · 2024
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