ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

Mathqa: Towards interpretable math word problem solving with operation-based formalisms

Original

Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 1905

Earlier work this paper cites.

Scaling laws for neural language models

Original

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001

Earlier work this paper cites.

Cognitive load theory and instructional design: Recent developments

Fred Paas, Alexander Renkl, and John Sweller. 2010 · 2010

Earlier work this paper cites.

Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning

Original

Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P Xing, and Liang Lin. 2021 · 2021

Earlier work this paper cites.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and 1 others. 2021 · 2021

Earlier work this paper cites.

Measuring mathematical problem solving with the math dataset

Original

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021

Earlier work this paper cites.

New generation deep learning for video object detection: A survey

Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, and Xu Tang. 2021 · 2021

Earlier work this paper cites.

Raise a child in large language model: Towards effective and generalizable fine-tuning

Original

Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021 · 2021

Earlier work this paper cites.

Visual representations in the human brain are aligned with large language models

Original

Adrien Doerig, Tim C Kietzmann, Emily Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, and Ian Charest. 2022 · 2022

Earlier work this paper cites.

Adafocal: Calibration-aware adaptive focal loss

Arindam Ghosh, Thomas Schaaf, and Matthew Gormley. 2022 · 2022

Earlier work this paper cites.

Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

Earlier work this paper cites.

Solving quantitative reasoning problems with language models

Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, and 1 others. 2022 · 2022

Earlier work this paper cites.

Generalized focal loss: Towards efficient representation learning for dense object detection

Xiang Li, Chengqi Lv, Wenhai Wang, Gang Li, Lingfeng Yang, and Jian Yang. 2022 · 2022

Earlier work this paper cites.

A survey of deep learning for mathematical reasoning

Original

Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, and Kai-Wei Chang. 2022 · 2022

Earlier work this paper cites.

Lila: A unified benchmark for mathematical reasoning

Original

Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and 1 others. 2022 · 2022

Earlier work this paper cites.

Memorization without overfitting: Analyzing the training dynamics of large language models

Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, and Armen Aghajanyan. 2022 · 2022

Earlier work this paper cites.

Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load

Amine Abbad-Andaloussi, Andrea Burattin, Tijs Slaats, Ekkart Kindler, and Barbara Weber. 2023 · 2023

Earlier work this paper cites.

Scaling laws for generative mixed-modal language models

Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, and Luke Zettlemoyer. 2023 · 2023

Earlier work this paper cites.

Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond

Original

Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023

Earlier work this paper cites.

Turning large language models into cognitive models

Original

Marcel Binz and Eric Schulz. 2023 · 2023

Earlier work this paper cites.

Theoremqa: A theorem-driven question answering dataset

Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, and Tony Xia. 2023a · 2023

Earlier work this paper cites.

G-llava: Solving geometric problem with multi-modal large language model

Original

Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, and 1 others. 2023 · 2023

Earlier work this paper cites.

Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes

Original

Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023

Earlier work this paper cites.

Geomverse: A systematic evaluation of large models for geometric reasoning

Original

Mehran Kazemi, Hamidreza Alvari, Ankit Anand, Jialin Wu, Xi Chen, and Radu Soricut. 2023 · 2023

Earlier work this paper cites.

Understanding the effects of rlhf on llm generalisation and diversity

Original

Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. 2023 · 2023

Earlier work this paper cites.

Query and response augmentation cannot help out-of-domain math reasoning generalization

Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, and Chang Zhou. 2023 · 2023

Earlier work this paper cites.

Visual instruction tuning

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023 · 2023

Earlier work this paper cites.

GPT-4 technical report

Original

OpenAI. 2023 · 2023

Earlier work this paper cites.

Elementary math learning through piaget’s cognitive development stages

Annabelle Rabillas, Osias Kit Kilag, Neil Cañete, Maria Trazona, Mery Lou Calope, and Jacqueline Kilag. 2023 · 2023

Earlier work this paper cites.

Cross-modal fine-tuning: Align then refine

Junhong Shen, Liam Li, Lucio M Dery, Corey Staten, Mikhail Khodak, Graham Neubig, and Ameet Talwalkar. 2023 · 2023

Earlier work this paper cites.

Detecting pretraining data from large language models

Original

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2023 · 2023

Earlier work this paper cites.

How to bridge the gap between modalities: A comprehensive survey on multimodal large language model

Original

Shezheng Song, Xiaopeng Li, and Shasha Li. 2023 · 2023

Earlier work this paper cites.

Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph

Original

Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023 · 2023

Cited alongside, same era.

Cogvlm: Visual expert for pretrained language models

Original

Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, and 1 others. 2023 · 2023

Cited alongside, same era.

Are deep neural networks adequate behavioral models of human visual perception?

Felix A Wichmann and Robert Geirhos. 2023 · 2023

Cited alongside, same era.

A survey of large language models

Original

Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, and 1 others. 2023 · 2023

Cited alongside, same era.

Large language models for information retrieval: A survey

Scieval: A multi-level large language model evaluation benchmark for scientific research

Liangtai Sun, Yang Han, Zihan Zhao, Da Ma, Zhennan Shen, Baocai Chen, Lu Chen, and Kai Yu. 2024 · 2024

Closest in time.

Qvq: To see the world with wisdom

Qwen Team. 2024 · 2024

Closest in time.

Solving olympiad geometry without human demonstrations

Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. 2024 · 2024

Closest in time.

Putnambench: Evaluating neural theorem-provers on the putnam mathematical competition

Original

George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, and Swarat Chaudhuri. 2024 · 2024

Closest in time.

Advances and challenges in meta-learning: A technical review

Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn Rognvaldsson, and KC Santosh. 2024 · 2024

Closest in time.