Fetching the paper…
Reading the bibliography…
When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT).
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
A diagram is worth a dozen images
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Min Joon Seo, Hannaneh Hajishirzi, and Ali Farhadi · 2016
Earlier work this paper cites.
Semantic parsing to probabilistic programs for situated question answering
Jayant Krishnamurthy, Oyvind Tafjord, and Aniruddha Kembhavi · 2016
Earlier work this paper cites.
Yin and Yang: Balancing and answering binary visual questions
Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2016
Earlier work this paper cites.
Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
From textbooks to knowledge: A case study in harvesting axiomatic knowledge from textbooks to solve geometry problems
Mrinmaya Sachan, Kumar Dubey, and Eric Xing · 2017
Earlier work this paper cites.
Learning to solve geometry problems from natural language demonstrations in textbooks
Mrinmaya Sachan and Eric Xing · 2017
Earlier work this paper cites.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
Earlier work this paper cites.
Question-guided hybrid convolution for visual question answering
Peng Gao, Hongsheng Li, Shuang Li, Pan Lu, Yikang Li, Steven CH Hoi, and Xiaogang Wang · 2018
Earlier work this paper cites.
Peter A Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton T Morrison · 2018
Earlier work this paper cites.
Dvqa: Understanding data visualizations via question answering
Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan · 2018
Earlier work this paper cites.
Bilinear attention networks
Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang · 2018
Earlier work this paper cites.
R-vqa: learning visual relation facts with semantic attention for visual question answering
Pan Lu, Lei Ji, Wei Zhang, Nan Duan, Ming Zhou, and Jianyong Wang · 2018
Earlier work this paper cites.
Co-attending free-form regions and detections with multi-modal multiplicative feature embedding for visual question answering
Pan Lu, Hongsheng Li, Wei Zhang, Jianyong Wang, and Xiaogang Wang · 2018
Cited alongside, same era.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
Cited alongside, same era.
Dynamic fusion with intra-and inter-modality attention flow for visual question answering
Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven CH Hoi, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Cited alongside, same era.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Later among the works it cites.
Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
Later among the works it cites.
What makes good in-context examples for gpt-
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
Later among the works it cites.
Inter-gps: Interpretable geometry problem solving with formal language and symbolic reasoning
Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Deep modular co-attention networks for visual question answering
Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton · 2020
Cited alongside, same era.
Harsh Jhamtani and Peter Clark · 2020
Cited alongside, same era.
Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou Yu, Xiaodan Liang, and Song-Chun Zhu · 2021
Later among the works it cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2021
Later among the works it cites.
Reframing instructional prompts to gptk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi · 2021
Later among the works it cites.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2021
Later among the works it cites.
Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
Later among the works it cites.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
Later among the works it cites.
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
Later among the works it cites.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
Later among the works it cites.
Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill · 2022
Closest in time.
Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan · 2022
Closest in time.
Lila: A unified benchmark for mathematical reasoning
Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and Ashwin Kalyan · 2022
Closest in time.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Closest in time.
In-BoXBART: Get instructions into biomedical multi-task learning
Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, Murad Mohammad, and Chitta Baral · 2022
Closest in time.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
Closest in time.