Fetching the paper…
Reading the bibliography…
Against the backdrop of enthusiasm for large language models (LLMs), there is a growing need to scientifically assess their capabilities and shortcomings.
Vizwiz: nearly real-time answers to visual questions
Jeffrey P Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, et al · 2010
Earlier work this paper cites.
A connection between partial symmetry and inverse procedural modeling
Martin Bokeloh, Michael Wand, and Hans-Peter Seidel · 2010
Earlier work this paper cites.
Inverse procedural modeling by automatic generation of L-systems
O. Št’ava, B. Beneš, R. Měch, D. G. Aliaga, and P. Krištof · 2010
Earlier work this paper cites.
Approximate Bayesian image interpretation using generative probabilistic graphics programs
Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, and Josh Tenenbaum · 2013
Earlier work this paper cites.
Inverse procedural modelling of trees
O. Št’ava, S. Pirk, J. Kratt, B. Chen, R. Měch, O. Deussen, and B. Benes · 2014
Earlier work this paper cites.
Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
Earlier work this paper cites.
Picture: A probabilistic programming language for scene perception
Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, and Vikash Mansinghka · 2015
Earlier work this paper cites.
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
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.
Neural scene de-rendering
Jiajun Wu, Joshua B. Tenenbaum, and Pushmeet Kohli · 2017
Earlier work this paper cites.
Xnli: Evaluating cross-lingual sentence representations
Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R Bowman, Holger Schwenk, and Veselin Stoyanov · 2018
Earlier work this paper cites.
InverseCSG: Automatic conversion of 3D models to CSG trees
Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Armando Solar-Lezama, and Wojciech Matusik · 2018
Earlier work this paper cites.
Learning to infer graphics programs from hand-drawn images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum · 2018
Earlier work this paper cites.
Synthesizing programs for images using reinforced adversarial learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, and Oriol Vinyals · 2018
Earlier work this paper cites.
Prolific.ac—a subject pool for online experiments
Stefan Palan and Christian Schitter · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Earlier work this paper cites.
CSGNet: Neural shape parser for constructive solid geometry
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji · 2018
Earlier work this paper cites.
Neural-symbolic VQA: Disentangling reasoning from vision and language understanding
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum · 2018
Earlier work this paper cites.
Towards automatic learning of procedures from web instructional videos
Luowei Zhou, Chenliang Xu, and Jason Corso · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Write, execute, assess: Program synthesis with a REPL
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Earlier work this paper cites.
Meta-Sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
Earlier work this paper cites.
ABC: A big CAD model dataset for geometric deep learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo · 2019
Earlier work this paper cites.
Learning to describe scenes with programs
Yunchao Liu, Jiajun Wu, Zheng Wu, Daniel Ritchie, William T. Freeman, and Joshua B. Tenenbaum · 2019
Earlier work this paper cites.
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu · 2019
Earlier work this paper cites.
Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
Earlier work this paper cites.
Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2019
Earlier work this paper cites.
Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
Earlier work this paper cites.
Learning to infer and execute 3D shape programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi · 2020
Earlier work this paper cites.
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.
Pymt5: multi-mode translation of natural language and python code with transformers
Colin B Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan · 2020
Cited alongside, same era.
Meta-Sim2: Unsupervised learning of scene structure for synthetic data generation
Jeevan Devaranjan, Amlan Kar, and Sanja Fidler · 2020
Cited alongside, same era.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Cited alongside, same era.
Inverse procedural modeling of branching structures by inferring l-systems
Jianwei Guo, Haiyong Jiang, Bedrich Benes, Oliver Deussen, Xiaopeng Zhang, Dani Lischinski, and Hui Huang · 2020
Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, and Yue Zhang · 2022
Later among the works it cites.
Leveraging large language models for scalable vector graphics-driven image understanding
Mu Cai, Zeyi Huang, Yuheng Li, Haohan Wang, and Yong Jae Lee · 2023
Later among the works it cites.
Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt
Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang · 2023
Later among the works it cites.
Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
Kevin Ellis, Lionel Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lore Anaya Pozo, Luke Hewitt, Armando Solar-Lezama, and Joshua B Tenenbaum · 2023
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.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Cited alongside, same era.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
Cited alongside, same era.
UCSG-NET–Unsupervised discovering of constructive solid geometry tree
Kacper Kania, Maciej Zieba, and Tomasz Kajdanowicz · 2020
Cited alongside, same era.
The child as hacker
Joshua S Rule, Joshua B Tenenbaum, and Steven T Piantadosi · 2020
Cited alongside, same era.
SketchGraphs: A large-scale dataset for modeling relational geometry in computer-aided design
Ari Seff, Yaniv Ovadia, Wenda Zhou, and Ryan P. Adams · 2020
Cited alongside, same era.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis · 2023
Later among the works it cites.
Improving unsupervised visual program inference with code rewriting families
Aditya Ganeshan, R. Kenny Jones, and Daniel Ritchie · 2023
Later among the works it cites.
Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan · 2023
Later among the works it cites.
Inferfix: End-to-end program repair with llms
Matthew Jin, Syed Shahriar, Michele Tufano, Xin Shi, Shuai Lu, Neel Sundaresan, and Alexey Svyatkovskiy · 2023
Later among the works it cites.
Api-bank: A benchmark for tool-augmented llms
Minghao Li, Feifan Song, Bowen Yu, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li · 2023
Later among the works it cites.
Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao · 2023
Later among the works it cites.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang · 2023
Later among the works it cites.
At which training stage does code data help llms reasoning?
Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, and Shanshan Li · 2023
Later among the works it cites.
Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2023
Later among the works it cites.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
Later among the works it cites.
Controlling text-to-image diffusion by orthogonal finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf · 2023
Later among the works it cites.
Neurosymbolic models for computer graphics
Daniel Ritchie, Paul Guerrero, R. Kenny Jones, Niloy J. Mitra, Adriana Schulz, Karl D. D. Willis, and Jiajun Wu · 2023
Later among the works it cites.
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
Later among the works it cites.
Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting · 2023
Later among the works it cites.
Copiloting the copilots: Fusing large language models with completion engines for automated program repair
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang · 2023
Later among the works it cites.
Lionel Wong, Gabriel Grand, Alexander K Lew, Noah D Goodman, Vikash K Mansinghka, Jacob Andreas, and Joshua B Tenenbaum · 2023
Later among the works it cites.
Cvalues: Measuring the values of chinese large language models from safety to responsibility
Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, et al · 2023
Later among the works it cites.
White-box compiler fuzzing empowered by large language models
Chenyuan Yang, Yinlin Deng, Runyu Lu, Jiayi Yao, Jiawei Liu, Reyhaneh Jabbarvand, and Lingming Zhang · 2023
Later among the works it cites.
Mammoth: Building math generalist models through hybrid instruction tuning
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen · 2023
Later among the works it cites.
Agieval: A human-centric benchmark for evaluating foundation models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan · 2023
Later among the works it cites.
Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou · 2023
Later among the works it cites.
To code, or not to code? exploring impact of code in pre-training
Viraat Aryabumi, Yixuan Su, Raymond Ma, Adrien Morisot, Ivan Zhang, Acyr Locatelli, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker · 2024
Closest in time.
Re-thinking inverse graphics with large language models
Peter Kulits, Haiwen Feng, Weiyang Liu, Victoria Abrevaya, and Michael J Black · 2024
Closest in time.
Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, YK Li, Yu Wu, and Daya Guo · 2024
Closest in time.
Open-instruct
VMware AI Labs · 2024
Closest in time.
Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, YU Jincheng, Zhengying Liu, Yu Zhang, James Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu · 2024
Closest in time.
Revisiting out-of-distribution robustness in nlp: Benchmarks, analysis, and llms evaluations
Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, and Maosong Sun · 2024
Closest in time.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2024
Closest in time.
Vgbench: Evaluating large language models on vector graphics understanding and generation
Bocheng Zou, Mu Cai, Jianrui Zhang, and Yong Jae Lee · 2024
Closest in time.