MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI, AI(8)

AI・機械学習関連論文 Calendar 2024
https://qiita.com/advent-calendar/2024/aiml
Day 8投稿予定記事です。

最初は、
LLM(Large Language Model) Advent Calendar 2024
https://qiita.com/advent-calendar/2024/llm
3日目投稿予定でした。

MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen
https://arxiv.org/abs/2311.16502v4

参考文献は、番号が一部抜けています。順次追記します。
単語帳は、一部、単語がうまく分割できず、現在、単語分解するか方法を検討中です。
今しばらくお待ちください。よい方法があれば、コメント欄にご記入いただけると幸いです。

<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words and/or centences in order.
### References
[1] Blaise Agu ̈era y Arcas and Peter Norvig. Artificial general intelligence is already here. Noema Magazine, 2023. 1
[2] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine
Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. In Advances in Neural Information Processing Systems, 2022. 3, 5
[3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. VQA: Visual Question Answering. In International Conference on Computer Vision (ICCV), 2015. 2, 3
[4] AnasAwadalla,IrenaGao,JoshGardner,JackHessel,Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al. Openflamingo: An open- source framework for training large autoregressive vision- language models. arXiv preprint arXiv:2308.01390, 2023. 3, 5, 6, 15, 16, 17, 18, 19, 20, 21
[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966, 2023. 5, 6, 7, 15, 16, 17, 18, 19, 20, 21
[6] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sag ̆nak Tas ̧ırlar. Introducing our multimodal models, 2023. 3, 5, 6, 7, 15, 16, 17, 18, 19, 20, 21
[7] Se ́bastienBubeck,VarunChandrasekaran,RonenEldan,Jo- hannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023. 1
[8] Bunny. Bunny-3b. https://github.com/cappuch/ Bunny-Qwen, 2024. GitHub Repository. 15, 16, 17, 18, 19, 20, 21
[9] Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Se- bastian Goodman, Xiao Wang, Yi Tay, et al. Pali-x: On scaling up a multilingual vision and language model. arXiv preprint arXiv:2305.18565, 2023. 2
[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European Conference on Computer Vision, pages 104–120, 2020. 3
[11] Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Zhong Muyan, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks. arXiv preprint arXiv:2312.14238, 2023. 6, 15, 16, 17, 18, 19, 20, 21
[12] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhang- hao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yong- hao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023. 3, 5, 6, 15, 16, 17, 18, 19, 20, 21
[13] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022. 1
[14] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. 3, 5, 6, 15, 16, 17, 18, 19, 20, 21
[15] Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Lin- jun Zhang, James Zou, and Huaxiu Yao. Holistic analysis of hallucination in gpt-4v (ision): Bias and interference chal- lenges. arXiv preprint arXiv:2311.03287, 2023. 3, 8
[16] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructblip: Towards general- purpose vision-language models with instruction tuning. arXiv preprint arXiv:2305.06500, 2023. 2, 3, 5, 6, 7, 15, 16, 17, 18, 19, 20, 21
[17] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, et al. Internlm-xcomposer2: Mastering free-form text-image composition and compre- hension in vision-language large model. arXiv preprint arXiv:2401.16420, 2024. 6, 15, 16, 17, 18, 19, 20, 21
[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representa- tions, 2021. 3
[19] Adept Fuyu Team. Adept fuyu-heavy: A new multimodal model. https://www.adept.ai/blog/adept- fuyu-heavy, 2024. 15, 16, 17, 18, 19, 20, 21
[20] Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xi- angyu Yue, et al. Llama-adapter v2: Parameter-efficient vi- sual instruction model. arXiv preprint arXiv:2304.15010, 2023. 3, 5
[21] Yingqiang Ge, Wenyue Hua, Jianchao Ji, Juntao Tan, Shuyuan Xu, and Yongfeng Zhang. Openagi: When llm meets domain experts. arXiv preprint arXiv:2304.04370, 2023. 1
[22] Google Gemini Team. Gemini: A family of highly capable multimodal models. https : / / storage . googleapis . com / deepmind - media / gemini / gemini_1_report.pdf, 2023. 15, 16, 17, 18, 19, 20, 21, 119
[23] Google Gemini Team. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. https: //storage.googleapis.com/deepmind-media/ gemini/gemini_v1_5_report.pdf, 2024. 6, 15, 119
[24] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Ba- tra, and Devi Parikh. Making the v in vqa matter: Elevating
the role of image understanding in visual question answer- ing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904–6913, 2017. 2, 3
[25] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Mea- suring massive multitask language understanding. In Inter- national Conference on Learning Representations, 2020. 2
[26] Yupan Huang, Zaiqiao Meng, Fangyu Liu, Yixuan Su, Col- lier Nigel, and Yutong Lu. Sparkles: Unlocking chats across multiple images for multimodal instruction-following mod- els. arXiv preprint arXiv:2308.16463, 2023. 3
[27] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 6700–6709, 2019. 3
[28] HyperGAI.Revolutionizingthefuturewithhypergenerative ai. 2024. 15, 16, 17, 18, 19, 20, 21
[29] ChaoJia,YinfeiYang,YeXia,Yi-TingChen,ZaranaParekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representa- tion learning with noisy text supervision. In International conference on machine learning, pages 4904–4916. PMLR, 2021. 3
[30] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in pho- tographs of natural scenes. In Proceedings of the 2014 con- ference on empirical methods in natural language processing (EMNLP), pages 787–798, 2014. 2
[31] Kunlun. Agi and aigc business skywork. 2024. 15, 16, 17, 18, 19, 20, 21
[32] Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu, Guoyu Lu, Sheng Li, Tianming Liu, and Xiaoming Zhai. Artificial general intelligence (agi) for edu- cation. arXiv preprint arXiv:2304.12479, 2023. 1
[33] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yix- iao Ge, and Ying Shan. Seed-bench: Benchmarking mul- timodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023. 2, 3
[34] Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, and Ziwei Liu. Otter: A multi-modal model with in-context instruction tuning. arXiv preprint arXiv:2305.03726, 2023. 3, 5, 15, 16, 17, 18, 19, 20, 21
[35] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. Inter- national Conference on Machine Learning, 2023. 2, 3, 5, 6, 7, 15, 16, 17, 18, 19, 20, 21
[36] Lei Li, Yuwei Yin, Shicheng Li, Liang Chen, Peiyi Wang, Shuhuai Ren, Mukai Li, Yazheng Yang, Jingjing Xu, Xu Sun, et al. M3it: A large-scale dataset towards multi- modal multilingual instruction tuning. arXiv preprint arXiv:2306.04387, 2023. 3
[37] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In Computer Vision–ECCV 2020:
16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXX 16, pages 121–137. Springer, 2020. 3
[38] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucina- tion in large vision-language models. arXiv preprint arXiv:2305.10355, 2023. 3
[39] Ji Lin, Hongxu Yin, Wei Ping, Yao Lu, Pavlo Molchanov, Andrew Tao, Huizi Mao, Jan Kautz, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. arXiv preprint arXiv:2312.07533, 2023. 6, 15, 16, 17, 18, 19, 20, 21
[40] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dolla ́r, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014. 2, 3
[41] ZiyiLin,ChrisLiu,RenruiZhang,PengGao,LongtianQiu, Han Xiao, Han Qiu, Chen Lin, Wenqi Shao, Keqin Chen, et al. Sphinx: The joint mixing of weights, tasks, and visual embeddings for multi-modal large language models. arXiv preprint arXiv:2311.07575, 2023. 15, 16, 17, 18, 19, 20, 21
[42] Fuxiao Liu, Tianrui Guan, Zongxia Li, Lichang Chen, Yaser Yacoob, Dinesh Manocha, and Tianyi Zhou. Hallusion- bench: You see what you think? or you think what you see? an image-context reasoning benchmark challenging for gpt- 4v (ision), llava-1.5, and other multi-modality models. arXiv preprint arXiv:2310.14566, 2023. 3
[43] Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. Aligning large multi-modal model with robust instruction tuning. arXiv preprint arXiv:2306.14565, 2023. 3
[44] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. arXiv preprint arXiv:2310.03744, 2023. 2, 3, 5, 7, 15, 16, 17, 18, 19, 20, 21
[45] HaotianLiu,ChunyuanLi,QingyangWu,andYongJaeLee. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023. 3
[46] Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Im- proved reasoning, ocr, and world knowledge. 2024. 6, 15, 16, 17, 18, 19, 20, 21
[47] YuanLiu,HaodongDuan,YuanhanZhang,BoLi,Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023. 2, 3
[48] Yuliang Liu, Zhang Li, Hongliang Li, Wenwen Yu, Mingxin Huang, Dezhi Peng, Mingyu Liu, Mingrui Chen, Chunyuan Li, Lianwen Jin, et al. On the hidden mystery of ocr in large multimodal models. arXiv preprint arXiv:2305.07895, 2023. 3
[49] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 3
[50] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35:2507–2521, 2022. 2
[51] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023. 3
[52] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 3
[53] Gre ́goireMialon,Cle ́mentineFourrier,CraigSwift,Thomas Wolf, Yann LeCun, and Thomas Scialom. Gaia: a benchmark for general ai assistants. arXiv preprint arXiv:2311.12983, 2023. 1, 3
[54] MiniCPM. Minicpm-v. https://github.com/ OpenBMB/MiniCPM, 2024. GitHub Repository. 15, 16, 17, 18, 19, 20, 21
[55] MiniCPM. Minicpm-v-2, 2024. 15, 16, 17, 18, 19, 20, 21
[56] Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat, Lin F Yang, and Kai-Wei Chang. Metavl: Transferring in-context learning ability from language models to vision-language models. arXiv preprint
arXiv:2306.01311, 2023. 3
[57] MeredithRingelMorris,JaschaSohl-dickstein,NoahFiedel,
Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, and Shane Legg. Levels of agi: Opera- tionalizing progress on the path to agi. arXiv preprint arXiv:2311.02462, 2023. 1, 3, 8
[58] OminiLMM. Ominilmm-12b. https://github.com/ OpenBMB/OmniLMM, 2024. GitHub Repository. 15, 16, 17, 18, 19, 20, 21
[59] OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1, 6, 15, 16, 17, 18, 19, 20, 21
[60] OpenAI. Gpt-4v(ision) system card, 2023. 2, 6, 7, 15, 16, 17, 18, 19, 20, 21
[61] OpenAI. Gpt-4o. 2024. 6, 15, 119
[62] Aitor Ormazabal, Che Zheng, Cyprien de Masson d’Autume,
Dani Yogatama, Deyu Fu, Donovan Ong, et al. Reka core, flash, and edge: A series of powerful multimodal language models. https://publications.reka.ai/reka- core-tech-report.pdf, 2024. 15, 16, 17, 18, 19, 20, 21, 119
[63] Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Ground- ing multimodal large language models to the world. arXiv preprint arXiv:2306.14824, 2023. 5, 6, 15, 16, 17, 18, 19, 20, 21
[64] Qwen. Qwen-vl-plus. https://github.com/ QwenLM/Qwen-VL?tab=readme-ov-file#qwen- vl-plus, 2023. GitHub Repository. 15, 16, 17, 18, 19, 20, 21
[65] Qwen. Qwen-vl-max. https : / / github . com / QwenLM/Qwen-VL?tab=readme-ov-file#qwen- vl-max, 2024. GitHub Repository. 6, 15, 16, 17, 18, 19, 20, 21
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, pages 8748–8763. PMLR, 2021. 3, 5
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information process- ing systems, 28, 2015. 3
sensenova. Sensechat-vision, 2024. 6, 15, 16, 17, 18, 19, 20, 21
Amanpreet Singh, Vivek Natarjan, Meet Shah, Yu Jiang, Xinlei Chen, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 8317–8326, 2019. 2
Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, et al. Generative multimodal models are in-context learners. arXiv preprint arXiv:2312.13286, 2023. 15, 16, 17, 18, 19, 20, 21
Hao Tan and Mohit Bansal. Lxmert: Learning cross- modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP- IJCNLP), pages 5100–5111, 2019. 3
Claude Team. Introducing the next generation of claude.
https://www.anthropic.com/news/claude-3- family, 2024. 6, 15, 119
InfiMM Team. Infimm: Advancing multimodal understand- ing from flamingo’s legacy through diverse llm integration, 2024. 15, 16, 17, 18, 19, 20, 21
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothe ́e Lacroix, Baptiste Rozie`re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 1, 5
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. 6, 15, 16, 17, 18, 19, 20, 21
Junyang Wang, Yiyang Zhou, Guohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Jihua Zhu, et al. Evaluation and analysis of hal- lucination in large vision-language models. arXiv preprint arXiv:2308.15126, 2023. 3
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al. Cogvlm: Visual expert for pretrained language models. arXiv preprint arXiv:2311.03079, 2023. 2, 5, 6, 15, 16, 17, 18, 19, 20, 21
[78] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia
Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. In International Conference on Learning Representations, 2021. 3
[79] Peng Xu, Wenqi Shao, Kaipeng Zhang, Peng Gao, Shuo Liu, Meng Lei, Fanqing Meng, Siyuan Huang, Yu Qiao, and Ping Luo. Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models. arXiv preprint arXiv:2306.09265, 2023. 3
[80] Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. The dawn of lmms: Preliminary explorations with gpt-4v (ision). arXiv preprint arXiv:2309.17421, 2023. 2
[81] Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al. mplug-owl: Modularization empowers large language models with multimodality. arXiv preprint arXiv:2304.14178, 2023. 3
[82] Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration. arXiv preprint arXiv:2311.04257, 2023. 3, 5, 15, 16, 17, 18, 19, 20, 21
[83] Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingn- ing Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, et al. Lamm: Language-assisted multi- modal instruction-tuning dataset, framework, and bench- mark. arXiv preprint arXiv:2306.06687, 2023. 2, 3
[84] AlexYoung,BeiChen,ChaoLi,ChengenHuang,GeZhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al. Yi: Open foundation models by 01. ai. arXiv preprint arXiv:2403.04652, 2024. 6, 15, 16, 17, 18, 19, 20, 21
[85] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mo- jtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. TMLR, 2022. 3
[86] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023. 2, 3
[87] Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5579–5588, 2021. 3
[88] Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199, 2023. 3, 6, 15, 16, 17, 18, 19, 20, 21
[89] Bo Zhao, Boya Wu, and Tiejun Huang. Svit: Scaling up visual instruction tuning. arXiv preprint arXiv:2307.04087, 2023. 3, 15, 16, 17, 18, 19, 20, 21

# term list

|no|wprd|count|
|:----|:----|:----|
|1|the|523|
|2|and|459|
|3|of|455|
|4|a|442|
|5|to|379|
|6|b|320|
|7|image|277|
|8|in|258|
|9|v|195|
|10|back|172|
|11|error|168|
|12|c|164|
|13|d|145|
|14|gpt|132|
|15|models|125|
|16|table|125|
|17|figure|124|
|18|is|119|
|19|arxiv|101|
|20|case|101|
|21|for|101|
|22|figures|96|
|23|on|95|
|24|sample|94|
|25|subfield|93|
|26|index|88|
|27|list|87|
|28|groundtruth|86|
|29|option|86|
|30|vl|82|
|31|t|81|
|32|as|80|
|33|we|80|
|34|with|76|
|35|reasoning|74|
|36|perceptual|66|
|37|s|64|
|38|category|62|
|39|mmmu|62|
|40|from|59|
|41|m|57|
|42|text|57|
|43|language|56|
|44|questions|56|
|45|expert|54|
|46|art|52|
|47|multimodal|52|
|48|correct|51|
|49|e|51|
|50|visual|51|
|51|knowledge|50|
|52|preprint|50|
|53|that|48|
|54|however|46|
|55|results|46|
|56|are|45|
|57|data|45|
|58|errorcategory|45|
|59|design|44|
|60|li|44|
|61|this|44|
|62|vision|43|
|63|by|42|
|64|llava|42|
|65|model|42|
|66|engineering|41|
|67|it|41|
|68|or|41|
|69|science|40|
|70|so|40|
|71|medicine|39|
|72|open|38|
|73|correctcase|37|
|74|benchmark|36|
|75|errorerrorreason|36|
|76|large|36|
|77|qwen|36|
|78|be|34|
|79|chat|34|
|80|history|34|
|81|wang|34|
|82|an|33|
|83|choice|33|
|84|different|33|
|85|lack|33|
|86|question|33|
|87|zhang|33|
|88|h|32|
|89|lmms|32|
|90|flan|31|
|91|xxl|31|
|92|chemistry|30|
|93|performance|30|
|94|types|30|
|95|r|29|
|96|these|29|
|97|best|28|
|98|gemini|28|
|99|images|27|
|100|liu|27|
|101|test|27|
|102|validation|27|
|103|perceptualerror|26|
|104|therefore|26|
|105|understanding|26|
|106|where|26|
|107|al|25|
|108|et|25|
|109|fuyu|25|
|110|ocr|25|
|111|shot|25|
|112|subjects|25|
|113|each|24|
|114|k|24|
|115|level|24|
|116|llama|24|
|117|music|24|
|118|only|24|
|119|blip|23|
|120|health|23|
|121|such|23|
|122|clinical|22|
|123|input|22|
|124|overall|22|
|125|reka|22|
|126|set|22|
|127|source|22|
|128|but|21|
|129|chen|21|
|130|our|21|
|131|vicuna|21|
|132|yi|21|
|133|agi|20|
|134|biology|20|
|135|both|20|
|136|computer|20|
|137|geography|20|
|138|has|20|
|139|instructblip|20|
|140|instruction|20|
|141|minicpm|20|
|142|mm|20|
|143|provided|20|
|144|subject|20|
|145|authors|19|
|146|benchmarks|19|
|147|can|19|
|148|conference|19|
|149|i|19|
|150|learning|19|
|151|llms|19|
|152|psychology|19|
|153|tasks|19|
|154|theory|19|
|155|answer|18|
|156|like|18|
|157|materials|18|
|158|multi|18|
|159|multiple|18|
|160|plus|18|
|161|should|18|
|162|which|18|
|163|appendix|17|
|164|caption|17|
|165|errors|17|
|166|internvl|17|
|167|math|17|
|168|th|17|
|169|thus|17|
|170|x|17|
|171|yu|17|
|172|adept|16|
|173|context|16|
|174|disciplines|16|
|175|economics|16|
|176|given|16|
|177|have|16|
|178|literature|16|
|179|more|16|
|180|paper|16|
|181|rad|16|
|182|reasoningerror|16|
|183|social|16|
|184|agriculture|15|
|185|all|15|
|186|analysis|15|
|187|college|15|
|188|dataset|15|
|189|human|15|
|190|its|15|
|191|let|15|
|192|lin|15|
|193|marco|15|
|194|openflamingo|15|
|195|other|15|
|196|physiology|15|
|197|specific|15|
|198|u|15|
|199|while|15|
|200|also|14|
|201|at|14|
|202|business|14|
|203|difficulty|14|
|204|domain|14|
|205|evaluation|14|
|206|foundation|14|
|207|information|14|
|208|ision|14|
|209|ln|14|
|210|not|14|
|211|otter|14|
|212|p|14|
|213|pro|14|
|214|their|14|
|215|tuning|14|
|216|xl|14|
|217|accounting|13|
|218|across|13|
|219|added|13|
|220|annotators|13|
|221|arrow|13|
|222|basic|13|
|223|between|13|
|224|challenges|13|
|225|electronics|13|
|226|github|13|
|227|hpt|13|
|228|https|13|
|229|management|13|
|230|marketing|13|
|231|nation|13|
|232|now|13|
|233|q|13|
|234|research|13|
|235|second|13|
|236|thecorrectansweris|13|
|237|there|13|
|238|towards|13|
|239|adapter|12|
|240|existing|12|
|241|few|12|
|242|finance|12|
|243|general|12|
|244|knowledgeerrorreason|12|
|245|lackofknowledge|12|
|246|lu|12|
|247|medium|12|
|248|norepinephrine|12|
|249|options|12|
|250|owl|12|
|251|pages|12|
|252|pathology|12|
|253|pharmacy|12|
|254|processing|12|
|255|random|12|
|256|shows|12|
|257|significant|12|
|258|sociology|12|
|259|st|12|
|260|tech|12|
|261|various|12|
|262|vqa|12|
|263|will|12|
|264|yang|12|
|265|annotation|11|
|266|based|11|
|267|been|11|
|268|bunny|11|
|269|challenging|11|
|270|cogvlm|11|
|271|crepe|11|
|272|diagnostics|11|
|273|diverse|11|
|274|frequent|11|
|275|heavy|11|
|276|humanities|11|
|277|kosmos|11|
|278|lens|11|
|279|max|11|
|280|mechanical|11|
|281|mechanics|11|
|282|medical|11|
|283|minigpt|11|
|284|mplug|11|
|285|no|11|
|286|report|11|
|287|subfields|11|
|288|total|11|
|289|training|11|
|290|type|11|
|291|ultra|11|
|292|wei|11|
|293|would|11|
|294|zhou|11|
|295|advanced|10|
|296|ai|10|
|297|animal|10|
|298|ca|10|
|299|cl|10|
|300|core|10|
|301|f|10|
|302|fine|10|
|303|flash|10|
|304|huang|10|
|305|infimm|10|
|306|internlm|10|
|307|laboratory|10|
|308|light|10|
|309|manage|10|
|310|modal|10|
|311|perception|10|
|312|performing|10|
|313|physics|10|
|314|pressure|10|
|315|problems|10|
|316|quality|10|
|317|representation|10|
|318|sensechat|10|
|319|sphinx|10|
|320|study|10|
|321|svit|10|
|322|team|10|
|323|textbooks|10|
|324|up|10|
|325|vila|10|
|326|xcomposer|10|
|327|abilities|9|
|328|annotated|9|
|329|architecture|9|
|330|author|9|
|331|claude|9|
|332|com|9|
|333|complex|9|
|334|control|9|
|335|example|9|
|336|experts|9|
|337|financial|9|
|338|first|9|
|339|if|9|
|340|interleaved|9|
|341|international|9|
|342|landsat|9|
|343|omnilmm|9|
|344|physical|9|
|345|point|9|
|346|preview|9|
|347|proceedings|9|
|348|process|9|
|349|project|9|
|350|public|9|
|351|recognition|9|
|352|see|9|
|353|selected|9|
|354|shares|9|
|355|sharesat|9|
|356|skywork|9|
|357|step|9|
|358|still|9|
|359|structure|9|
|360|sun|9|
|361|textual|9|
|362|through|9|
|363|tyramine|9|
|364|without|9|
|365|world|9|
|366|xu|9|
|367|you|9|
|368|yue|9|
|369|achieve|8|
|370|additionally|8|
|371|advantage|8|
|372|answers|8|
|373|any|8|
|374|bold|8|
|375|capabilities|8|
|376|cm|8|
|377|collection|8|
|378|comparative|8|
|379|contributed|8|
|380|cost|8|
|381|depth|8|
|382|detailed|8|
|383|diagrams|8|
|384|discipline|8|
|385|easy|8|
|386|edge|8|
|387|emu|8|
|388|evaluating|8|
|389|format|8|
|390|g|8|
|391|gao|8|
|392|historyquestion|8|
|393|including|8|
|394|intelligence|8|
|395|into|8|
|396|json|8|
|397|kai|8|
|398|kj|8|
|399|most|8|
|400|nh|8|
|401|one|8|
|402|peng|8|
|403|plant|8|
|404|playground|8|
|405|power|8|
|406|representations|8|
|407|significantly|8|
|408|systems|8|
|409|underlined|8|
|410|when|8|
|411|worst|8|
|412|wu|8|
|413|xiang|8|
|414|zephyr|8|
|415|zhao|8|
|416|accuracy|7|
|417|air|7|
|418|airbase|7|
|419|approximately|7|
|420|aqua|7|
|421|artificial|7|
|422|computerscience|7|
|423|distribution|7|
|424|drug|7|
|425|due|7|
|426|effect|7|
|427|energy|7|
|428|explanation|7|
|429|further|7|
|430|inorganic|7|
|431|ji|7|
|432|lei|7|
|433|less|7|
|434|market|7|
|435|mmhg|7|
|436|natural|7|
|437|next|7|
|438|pe|7|
|439|pre|7|
|440|production|7|
|441|response|7|
|442|se|7|
|443|sources|7|
|444|su|7|
|445|theansweris|7|
|446|transaction|7|
|447|understand|7|
|448|university|7|
|449|veh|7|
|450|very|7|
|451|y|7|
|452|year|7|
|453|zheng|7|
|454|among|6|
|455|bd|6|
|456|breadth|6|
|457|broad|6|
|458|captioning|6|
|459|cause|6|
|460|ch|6|
|461|chang|6|
|462|chemical|6|
|463|chunyuan|6|
|464|comprehensive|6|
|465|consider|6|
|466|direction|6|
|467|ended|6|
|468|european|6|
|469|evaluate|6|
|470|examples|6|
|471|file|6|
|472|fixed|6|
|473|flamingo|6|
|474|fluid|6|
|475|following|6|
|476|genetics|6|
|477|han|6|
|478|hard|6|
|479|hu|6|
|480|interpretation|6|
|481|j|6|
|482|jianfeng|6|
|483|key|6|
|484|leading|6|
|485|lee|6|
|486|massive|6|
|487|mathematical|6|
|488|mc|6|
|489|minimal|6|
|490|n|6|
|491|ni|6|
|492|oc|6|
|493|organic|6|
|494|over|6|
|495|painting|6|
|496|pattern|6|
|497|perform|6|
|498|relatively|6|
|499|scaling|6|
|500|terra|6|
|501|thescaleofthephotographsis|6|
|502|trade|6|
|503|two|6|
|504|use|6|
|505|weget|6|
|506|well|6|
|507|were|6|
|508|what|6|
|509|ye|6|
|510|yin|6|
|511|zhu|6|
|512|adults|5|
|513|advances|5|
|514|after|5|
|515|answering|5|
|516|available|5|
|517|baselines|5|
|518|because|5|
|519|besides|5|
|520|blood|5|
|521|calculus|5|
|522|carbon|5|
|523|charts|5|
|524|clinicalmedicine|5|
|525|collected|5|
|526|collecting|5|
|527|common|5|
|528|conducted|5|
|529|cover|5|
|530|cpu|5|
|531|critical|5|
|532|curation|5|
|533|current|5|
|534|designed|5|
|535|double|5|
|536|dynamics|5|
|537|efficient|5|
|538|epidemiology|5|
|539|experiments|5|
|540|fields|5|
|541|find|5|
|542|focus|5|
|543|forstate|5|
|544|fp|5|
|545|ge|5|
|546|graph|5|
|547|here|5|
|548|hox|5|
|549|improvement|5|
|550|instances|5|
|551|instead|5|
|552|levels|5|
|553|lijuan|5|
|554|ma|5|
|555|made|5|
|556|main|5|
|557|major|5|
|558|mi|5|
|559|microbiology|5|
|560|might|5|
|561|modality|5|
|562|nature|5|
|563|often|5|
|564|paintings|5|
|565|pan|5|
|566|part|5|
|567|per|5|
|568|pharmacology|5|
|569|phenelzine|5|
|570|photographs|5|
|571|principle|5|
|572|progress|5|
|573|protocol|5|
|574|range|5|
|575|reject|5|
|576|repository|5|
|577|require|5|
|578|requires|5|
|579|root|5|
|580|scale|5|
|581|secondary|5|
|582|section|5|
|583|sheng|5|
|584|signal|5|
|585|since|5|
|586|single|5|
|587|six|5|
|588|skilled|5|
|589|state|5|
|590|statistics|5|
|591|students|5|
|592|synthesis|5|
|593|system|5|
|594|tables|5|
|595|three|5|
|596|units|5|
|597|updated|5|
|598|within|5|
|599|yan|5|
|600|yes|5|
|601|ab|4|
|602|absolute|4|
|603|accurate|4|
|604|anatomy|4|
|605|ande|4|
|606|approach|4|
|607|arts|4|
|608|assess|4|
|609|atom|4|
|610|average|4|
|611|axis|4|
|612|bandc|4|
|613|barrel|4|
|614|batra|4|
|615|biochemistry|4|
|616|block|4|
|617|bo|4|
|618|bothtimeseriesaremeanstationary|4|
|619|breakdown|4|
|620|calculations|4|
|621|cao|4|
|622|cardiovascular|4|
|623|change|4|
|624|chemistryquestion|4|
|625|civil|4|
|626|co|4|
|627|cocaine|4|
|628|comparison|4|
|629|compiler|4|
|630|complexity|4|
|631|considerations|4|
|632|contamination|4|
|633|contemporary|4|
|634|contribution|4|
|635|converging|4|
|636|copyright|4|
|637|corporate|4|
|638|correctly|4|
|639|covering|4|
|640|dec|4|
|641|deliberate|4|
|642|derive|4|
|643|devi|4|
|644|diagnosticsandlabmedicine|4|
|645|directlabor|4|
|646|eliminate|4|
|647|embedding|4|
|648|end|4|
|649|eng|4|
|650|eric|4|
|651|even|4|
|652|exams|4|
|653|finishes|4|
|654|follow|4|
|655|foot|4|
|656|force|4|
|657|fpr|4|
|658|gap|4|
|659|generation|4|
|660|geometric|4|
|661|hallucination|4|
|662|handling|4|
|663|hao|4|
|664|heterogeneous|4|
|665|highly|4|
|666|huan|4|
|667|identify|4|
|668|ieee|4|
|669|indexvalueatt|4|
|670|introduces|4|
|671|joint|4|
|672|kevin|4|
|673|law|4|
|674|lead|4|
|675|liang|4|
|676|linjie|4|
|677|llm|4|
|678|long|4|
|679|machine|4|
|680|macroeconomics|4|
|681|mapping|4|
|682|mask|4|
|683|means|4|
|684|meng|4|
|685|ming|4|
|686|minor|4|
|687|mri|4|
|688|must|4|
|689|necessary|4|
|690|neural|4|
|691|neuropathology|4|
|692|new|4|
|693|non|4|
|694|number|4|
|695|o|4|
|696|objects|4|
|697|ofhand|4|
|698|openai|4|
|699|optical|4|
|700|outputs|4|
|701|parallel|4|
|702|parikh|4|
|703|path|4|
|704|perfect|4|
|705|personyears|4|
|706|peter|4|
|707|pharmaceutical|4|
|708|photo|4|
|709|physiologyquestion|4|
|710|present|4|
|711|presents|4|
|712|pretraining|4|
|713|probability|4|
|714|prompt|4|
|715|provide|4|
|716|qwenvl|4|
|717|radiology|4|
|718|regions|4|
|719|resources|4|
|720|role|4|
|721|samples|4|
|722|sci|4|
|723|selection|4|
|724|shi|4|
|725|show|4|
|726|shown|4|
|727|skills|4|
|728|solution|4|
|729|some|4|
|730|song|4|
|731|specialized|4|
|732|stage|4|
|733|starts|4|
|734|statement|4|
|735|states|4|
|736|strong|4|
|737|structures|4|
|738|surveying|4|
|739|tail|4|
|740|than|4|
|741|thecorrectoptionis|4|
|742|thedouble|4|
|743|thefatheriscomparedtoagypsy|4|
|744|thegeographicextentofthemonetizationofeurasianeconomies|4|
|745|theorderisfromlefttoright|4|
|746|thermodynamics|4|
|747|thespeedofaishalfthatofb|4|
|748|they|4|
|749|tianyu|4|
|750|tn|4|
|751|toptobottom|4|
|752|tuned|4|
|753|using|4|
|754|vaultedroofing|4|
|755|velocity|4|
|756|vet|4|
|757|wenhu|4|
|758|whichofthefollowingisthemostlikelydiagnosis|4|
|759|whorl|4|
|760|work|4|
|761|yuan|4|
|762|yuansheng|4|
|763|zero|4|
|764|ability|3|
|765|achieves|3|
|766|add|3|
|767|addition|3|
|768|adhere|3|
|769|adherence|3|
|770|administration|3|
|771|adopted|3|
|772|adult|3|
|773|aligning|3|
|774|andc|3|
|775|andstate|3|
|776|aob|3|
|777|aorta|3|
|778|architectureandengineering|3|
|779|area|3|
|780|areproductionzonesofprovenoilreserves|3|
|781|around|3|
|782|arttheory|3|
|783|aspect|3|
|784|atestoftwoindependentmeans|3|
|785|att|3|
|786|attention|3|
|787|au|3|
|788|augmented|3|
|789|bai|3|
|790|beam|3|
|791|being|3|
|792|believe|3|
|793|better|3|
|794|beyond|3|
|795|biological|3|
|796|bod|3|
|797|body|3|
|798|bothmovetostate|3|
|799|buttheydifferoninput|3|
|800|capability|3|
|801|capable|3|
|802|carefully|3|
|803|categories|3|
|804|cbt|3|
|805|cell|3|
|806|changes|3|
|807|chao|3|
|808|character|3|
|809|child|3|
|810|choosingthematchingterm|3|
|811|chung|3|
|812|classical|3|
|813|clef|3|
|814|clip|3|
|815|cod|3|
|816|comics|3|
|817|conclusion|3|
|818|consistent|3|
|819|contain|3|
|820|contains|3|
|821|contributions|3|
|822|crepes|3|
|823|criticism|3|
|824|ctr|3|
|825|cui|3|
|826|dandy|3|
|827|deep|3|
|828|demonstrate|3|
|829|details|3|
|830|determine|3|
|831|develop|3|
|832|development|3|
|833|dhruv|3|
|834|diagram|3|
|835|directmaterials|3|
|836|discussions|3|
|837|disease|3|
|838|do|3|
|839|dong|3|
|840|drama|3|
|841|drawing|3|
|842|duan|3|
|843|econometrics|3|
|844|effects|3|
|845|egoism|3|
|846|electrocardiography|3|
|847|elements|3|
|848|empirical|3|
|849|employed|3|
|850|encoder|3|
|851|engineeringquestion|3|
|852|ensure|3|
|853|epidemiologyquestion|3|
|854|essential|3|
|855|ethical|3|
|856|evident|3|
|857|evolution|3|
|858|exchange|3|
|859|expertise|3|
|860|expressions|3|
|861|fails|3|
|862|falsepositives|3|
|863|family|3|
|864|features|3|
|865|findings|3|
|866|formats|3|
|867|four|3|
|868|fromthetable|3|
|869|fundamental|3|
|870|future|3|
|871|generative|3|
|872|genes|3|
|873|geographyquestion|3|
|874|geometry|3|
|875|go|3|
|876|goal|3|
|877|gqa|3|
|878|graphic|3|
|879|grounding|3|
|880|haiyang|3|
|881|haotian|3|
|882|he|3|
|883|head|3|
|884|heart|3|
|885|height|3|
|886|higher|3|
|887|how|3|
|888|iii|3|
|889|illustrated|3|
|890|imperialist|3|
|891|importance|3|
|892|improved|3|
|893|incorrect|3|
|894|increase|3|
|895|indicating|3|
|896|industrial|3|
|897|inputs|3|
|898|insights|3|
|899|insteadof|3|
|900|instructions|3|
|901|introduce|3|
|902|investment|3|
|903|involve|3|
|904|isms|3|
|905|itislikelytobesn|3|
|906|jae|3|
|907|jingjing|3|
|908|jun|3|
|909|junyang|3|
|910|justinian|3|
|911|kpa|3|
|912|layer|3|
|913|led|3|
|914|length|3|
|915|licensing|3|
|916|limitations|3|
|917|line|3|
|918|linear|3|
|919|literaturequestion|3|
|920|lower|3|
|921|making|3|
|922|many|3|
|923|maoi|3|
|924|maturenewborn|3|
|925|may|3|
|926|measure|3|
|927|mechanicalengineering|3|
|928|meet|3|
|929|meticulously|3|
|930|mmbench|3|
|931|modalities|3|
|932|modern|3|
|933|months|3|
|934|musicquestion|3|
|935|mutant|3|
|936|name|3|
|937|naming|3|
|938|need|3|
|939|needs|3|
|940|netincome|3|
|941|notable|3|
|942|note|3|
|943|object|3|
|944|oninput|3|
|945|online|3|
|946|ophthalmic|3|
|947|opportunity|3|
|948|optics|3|
|949|opus|3|
|950|out|3|
|951|overhead|3|
|952|overheadrate|3|
|953|oxygen|3|
|954|pathologyquestion|3|
|955|pdf|3|
|956|phentolamine|3|
|957|photography|3|
|958|possible|3|
|959|posterior|3|
|960|prior|3|
|961|proprietary|3|
|962|publichealth|3|
|963|qinghao|3|
|964|qiu|3|
|965|queries|3|
|966|quizzes|3|
|967|rather|3|
|968|rays|3|
|969|redistribution|3|
|970|reduction|3|
|971|reference|3|
|972|region|3|
|973|regulations|3|
|974|relatedhand|3|
|975|ren|3|
|976|renrui|3|
|977|represents|3|
|978|respiratory|3|
|979|responses|3|
|980|room|3|
|981|rule|3|
|982|ruoqi|3|
|983|rupturedberryaneurysm|3|
|984|savior|3|
|985|scans|3|
|986|scienceqa|3|
|987|sciences|3|
|988|seed|3|
|989|seen|3|
|990|sense|3|
|991|separate|3|
|992|shapes|3|
|993|sheets|3|
|994|short|3|
|995|shortpastern|3|
|996|solvefor|3|
|997|space|3|
|998|standard|3|
|999|starting|3|
|1000|startsandfinisheswithoutanyinterleaving|3|
|1001|stateerror|3|
|1002|steven|3|
|1003|strategic|3|
|1004|structural|3|
|1005|substantial|3|
|1006|suggests|3|
|1007|sulfur|3|
|1008|supervision|3|
|1009|surgery|3|
|1010|symptom|3|
|1011|tallbacksofchairsandlampsatthecornersofdiningtables|3|
|1012|tan|3|
|1013|task|3|
|1014|theconfigurationatc|3|
|1015|them|3|
|1016|then|3|
|1017|thepainting|3|
|1018|thepontomedullaryjunction|3|
|1019|theyarenotequivalent|3|
|1020|third|3|
|1021|thomas|3|
|1022|those|3|
|1023|top|3|
|1024|totaldirectlabordollars|3|
|1025|totalfactoryoverhead|3|
|1026|typically|3|
|1027|typo|3|
|1028|united|3|
|1029|usingtheequation|3|
|1030|visionmayberestoredwithconcavelensandrefractivesurgery|3|
|1031|walkersyndrome|3|
|1032|way|3|
|1033|weighted|3|
|1034|wide|3|
|1035|writing|3|
|1036|xi|3|
|1037|xia|3|
|1038|xiao|3|
|1039|yiyang|3|
|1040|yong|3|
|1041|yuanhan|3|
|1042|_|2|
|1043|_report|2|
|1044|aandb|2|
|1045|abarbicanandbattlements|2|
|1046|able|2|
|1047|above|2|
|1048|abstract|2|
|1049|accepting|2|
|1050|accuracies|2|
|1051|accurately|2|
|1052|address|2|
|1053|adjacent|2|
|1054|adrenergic|2|
|1055|advancements|2|
|1056|adversarial|2|
|1057|advertisements|2|
|1058|advice|2|
|1059|agieval|2|
|1060|ahigherrooftomakeupfortheshortcolumns|2|
|1061|ahmed|2|
|1062|aim|2|
|1063|aims|2|
|1064|algebra|2|
|1065|algorithm|2|
|1066|align|2|
|1067|along|2|
|1068|alpha|2|
|1069|ambiguities|2|
|1070|amoatandcrenellations|2|
|1071|amothertellshersontostopwhining|2|
|1072|amount|2|
|1073|analog|2|
|1074|analyzed|2|
|1075|anda|2|
|1076|andcpu|2|
|1077|anddemocraticgovernments|2|
|1078|andm|2|
|1079|andmouthdiseaseintheplacebogroup|2|
|1080|andn|2|
|1081|andrequiresfurtherwork|2|
|1082|andsoon|2|
|1083|andthenthefirstonemightresume|2|
|1084|andthere|2|
|1085|andthisremainsunchanged|2|
|1086|aneurysm|2|
|1087|angle|2|
|1088|annotations|2|
|1089|another|2|
|1090|answererrorreason|2|
|1091|antagonist|2|
|1092|anterior|2|
|1093|anxietydisorder|2|
|1094|aojun|2|
|1095|apply|2|
|1096|areas|2|
|1097|artsquestion|2|
|1098|asaresult|2|
|1099|ascending|2|
|1100|assistedinsitukeratomileusis|2|
|1101|associated|2|
|1102|attachedgroupsandtheiratomicnumbers|2|
|1103|avoid|2|
|1104|avoided|2|
|1105|bansal|2|
|1106|baptiste|2|
|1107|bar|2|
|1108|basedmethod|2|
|1109|basedontheimageprovided|2|
|1110|basicmedicalscience|2|
|1111|beginningretainedearnings|2|
|1112|benchmarking|2|
|1113|beta|2|
|1114|betweensphere|2|
|1115|bias|2|
|1116|biodiversity|2|
|1117|biostatistics|2|
|1118|blocked|2|
|1119|blocks|2|
|1120|bone|2|
|1121|botany|2|
|1122|boyuan|2|
|1123|breast|2|
|1124|bridge|2|
|1125|brownstemrot|2|
|1126|butitfailedtocorrectlymaptheidstothecorrespondingillustrationsinthefigure|2|
|1127|cal|2|
|1128|calculatethemanufacturingcostperunitforproducta|2|
|1129|calculatethetotalmanufacturingcostforproducta|2|
|1130|calculatethetotaloverheadrate|2|
|1131|calculatethework|2|
|1132|calculation|2|
|1133|calculusquestion|2|
|1134|candd|2|
|1135|cartoon|2|
|1136|cartoons|2|
|1137|cases|2|
|1138|categorize|2|
|1139|cbtismoreeffectivethannotreatmentandmoreeffectivethanmeditation|2|
|1140|cbtisnotaseffectiveasmeditation|2|
|1141|cd|2|
|1142|chains|2|
|1143|challenge|2|
|1144|challenginghimtoconsiderthemultitudeofinterpretationsthepaintingrepresents|2|
|1145|chaotic|2|
|1146|cheng|2|
|1147|chi|2|
|1148|children|2|
|1149|chlorine|2|
|1150|chris|2|
|1151|chun|2|
|1152|cini|2|
|1153|circuit|2|
|1154|circulatory|2|
|1155|clark|2|
|1156|clc|2|
|1157|clearly|2|
|1158|close|2|
|1159|closed|2|
|1160|coauthors|2|
|1161|coca|2|
|1162|collaboration|2|
|1163|collect|2|
|1164|collectiveeffervescence|2|
|1165|commonsense|2|
|1166|completes|2|
|1167|comprehension|2|
|1168|comprising|2|
|1169|concave|2|
|1170|conceived|2|
|1171|conceptualization|2|
|1172|conghui|2|
|1173|considered|2|
|1174|consistency|2|
|1175|consistently|2|
|1176|consumerismandnationalidentities|2|
|1177|contrast|2|
|1178|converge|2|
|1179|copying|2|
|1180|corresponding|2|
|1181|correspondstoregionssuchasnortherncanadaandpartsofrussia|2|
|1182|cotton|2|
|1183|could|2|
|1184|covers|2|
|1185|creating|2|
|1186|cross|2|
|1187|crucial|2|
|1188|ct|2|
|1189|cu|2|
|1190|cvf|2|
|1191|dai|2|
|1192|daily|2|
|1193|datasets|2|
|1194|dawn|2|
|1195|decorativerhythmandrepetition|2|
|1196|decrease|2|
|1197|deeply|2|
|1198|deepmind|2|
|1199|default|2|
|1200|definition|2|
|1201|degradation|2|
|1202|degrees|2|
|1203|dehghani|2|
|1204|demonstrates|2|
|1205|dental|2|
|1206|depositionequilibrium|2|
|1207|designquestion|2|
|1208|designs|2|
|1209|despair|2|
|1210|detection|2|
|1211|determinethechangeininternalenergy|2|
|1212|deterministicfiniteautomaton|2|
|1213|dev|2|
|1214|developed|2|
|1215|diastolic|2|
|1216|difficult|2|
|1217|difficulties|2|
|1218|digital|2|
|1219|diminished|2|
|1220|direct|2|
|1221|disparity|2|
|1222|disred|2|
|1223|distributions|2|
|1224|dividends|2|
|1225|documents|2|
|1226|doesn|2|
|1227|doing|2|
|1228|domainis|2|
|1229|domains|2|
|1230|dongxu|2|
|1231|du|2|
|1232|duetothelackofspecificknowledgeabout|2|
|1233|dynamicsquestion|2|
|1234|eachofmass|2|
|1235|easyquestion|2|
|1236|eccv|2|
|1237|ecology|2|
|1238|effectively|2|
|1239|ehub|2|
|1240|eisblue|2|
|1241|electrical|2|
|1242|electromagnetism|2|
|1243|elementary|2|
|1244|embeddings|2|
|1245|energyandpower|2|
|1246|enhanced|2|
|1247|enhancements|2|
|1248|enhancing|2|
|1249|equilibrium|2|
|1250|ers|2|
|1251|etc|2|
|1252|evalai|2|
|1253|exhibit|2|
|1254|exists|2|
|1255|explanations|2|
|1256|explicitly|2|
|1257|expressed|2|
|1258|extensive|2|
|1259|external|2|
|1260|extraction|2|
|1261|faisal|2|
|1262|falls|2|
|1263|faster|2|
|1264|fe|2|
|1265|fiction|2|
|1266|finding|2|
|1267|firstly|2|
|1268|flawed|2|
|1269|focal|2|
|1270|focallength|2|
|1271|follows|2|
|1272|forc|2|
|1273|forchoice|2|
|1274|forexample|2|
|1275|foribssuffererswithoutananxietydisorder|2|
|1276|formula|2|
|1277|forsphere|2|
|1278|found|2|
|1279|framework|2|
|1280|free|2|
|1281|fromthegivenimage|2|
|1282|fromwhich|2|
|1283|frozen|2|
|1284|furu|2|
|1285|fuxiao|2|
|1286|gaia|2|
|1287|gas|2|
|1288|gene|2|
|1289|geneinamousewasreplacedwithahox|2|
|1290|geneticsquestion|2|
|1291|geotechnical|2|
|1292|give|2|
|1293|giventhis|2|
|1294|gives|2|
|1295|google|2|
|1296|googleapis|2|
|1297|goyal|2|
|1298|gpa|2|
|1299|groundheight|2|
|1300|group|2|
|1301|guardcells|2|
|1302|guo|2|
|1303|guohai|2|
|1304|haiku|2|
|1305|handpart|2|
|1306|haodong|2|
|1307|hardsubject|2|
|1308|heat|2|
|1309|help|2|
|1310|helping|2|
|1311|hence|2|
|1312|hg|2|
|1313|hidden|2|
|1314|hierarchical|2|
|1315|hierarchicalscale|2|
|1316|highlight|2|
|1317|highlights|2|
|1318|hoi|2|
|1319|holistic|2|
|1320|horizontal|2|
|1321|horror|2|
|1322|houdong|2|
|1323|hugo|2|
|1324|humanpapillomavirusinfection|2|
|1325|humans|2|
|1326|hydrogen|2|
|1327|hypertensive|2|
|1328|hyung|2|
|1329|ican|2|
|1330|ifahox|2|
|1331|immediacy|2|
|1332|immunology|2|
|1333|improve|2|
|1334|inaccuracies|2|
|1335|incidencedensity|2|
|1336|include|2|
|1337|included|2|
|1338|includes|2|
|1339|incorrectly|2|
|1340|increases|2|
|1341|indeed|2|
|1342|indicate|2|
|1343|indicates|2|
|1344|initial|2|
|1345|instance|2|
|1346|intercept|2|
|1347|interface|2|
|1348|interleavedprocessingoccurswhentwotransactionsareprocessedalternately|2|
|1349|interleaving|2|
|1350|internet|2|
|1351|interpret|2|
|1352|interval|2|
|1353|inthepoliticalcartoon|2|
|1354|inthepradomuseuminmadrid|2|
|1355|inthesecondimage|2|
|1356|inthestudyofkingphilipiv|2|
|1357|intricate|2|
|1358|introducing|2|
|1359|introduction|2|
|1360|involving|2|
|1361|io|2|
|1362|ipit|2|
|1363|ipitdisplaysastrongseasonality|2|
|1364|isminimal|2|
|1365|istheaccelerationduetogravity|2|
|1366|italy|2|
|1367|jack|2|
|1368|jacob|2|
|1369|james|2|
|1370|jiabo|2|
|1371|jiahui|2|
|1372|jiaming|2|
|1373|jiang|2|
|1374|jiasen|2|
|1375|jingren|2|
|1376|jointly|2|
|1377|junnan|2|
|1378|kgak|2|
|1379|kln|2|
|1380|labor|2|
|1381|lamm|2|
|1382|laser|2|
|1383|later|2|
|1384|lawrence|2|
|1385|le|2|
|1386|leaderboard|2|
|1387|leads|2|
|1388|leaving|2|
|1389|left|2|
|1390|leftventricle|2|
|1391|legg|2|
|1392|lewis|2|
|1393|limit|2|
|1394|limited|2|
|1395|linguistic|2|
|1396|llfollowthesesteps|2|
|1397|lmm|2|
|1398|log|2|
|1399|logic|2|
|1400|lone|2|
|1401|longer|2|
|1402|longpasternbone|2|
|1403|lookatthesituationinthe|2|
|1404|low|2|
|1405|luo|2|
|1406|lvlm|2|
|1407|lxmert|2|
|1408|macroeconomicsquestion|2|
|1409|magnitude|2|
|1410|managerial|2|
|1411|manuscript|2|
|1412|mao|2|
|1413|maps|2|
|1414|marks|2|
|1415|mathvista|2|
|1416|meaningthatonestarts|2|
|1417|meanwhile|2|
|1418|measuring|2|
|1419|mechanicsquestion|2|
|1420|meconiumaspirationsyndrome|2|
|1421|media|2|
|1422|medicinal|2|
|1423|mediumsubject|2|
|1424|meeting|2|
|1425|metavl|2|
|1426|methods|2|
|1427|mfroma|2|
|1428|mg|2|
|1429|middle|2|
|1430|mishra|2|
|1431|mitralregurgitation|2|
|1432|mmicl|2|
|1433|mmocr|2|
|1434|modernhistory|2|
|1435|module|2|
|1436|mohammad|2|
|1437|monochromatic|2|
|1438|moreover|2|
|1439|morris|2|
|1440|mostafa|2|
|1441|moving|2|
|1442|mukai|2|
|1443|multilingual|2|
|1444|multimodality|2|
|1445|nano|2|
|1446|narrative|2|
|1447|nationxhascomparativeadvantageinpaperproductionandshouldtradepapertonationyinexchangeforcrepes|2|
|1448|nationygivesupproducing|2|
|1449|network|2|
|1450|neurosciences|2|
|1451|nogpt|2|
|1452|noneoftheotheranswers|2|
|1453|normally|2|
|1454|notsure|2|
|1455|nuclear|2|
|1456|occur|2|
|1457|offered|2|
|1458|ohm|2|
|1459|ok|2|
|1460|ominilmm|2|
|1461|once|2|
|1462|ones|2|
|1463|openbmb|2|
|1464|operating|2|
|1465|opportunitycostof|2|
|1466|oppressor|2|
|1467|optic|2|
|1468|oscar|2|
|1469|others|2|
|1470|ov|2|
|1471|overview|2|
|1472|pairs|2|
|1473|panningblur|2|
|1474|parameter|2|
|1475|participated|2|
|1476|pathophysiology|2|
|1477|patientswithnon|2|
|1478|patterns|2|
|1479|pengcheng|2|
|1480|pengchuan|2|
|1481|percentile|2|
|1482|personality|2|
|1483|peutz|2|
|1484|phalanx|2|
|1485|photos|2|
|1486|photoscale|2|
|1487|phrases|2|
|1488|ping|2|
|1489|piotr|2|
|1490|pivotal|2|
|1491|played|2|
|1492|plots|2|
|1493|pmlr|2|
|1494|poetry|2|
|1495|pointe|2|
|1496|pointf|2|
|1497|posed|2|
|1498|poses|2|
|1499|potential|2|
|1500|presence|2|
|1501|price|2|
|1502|primarily|2|
|1503|primary|2|
|1504|principles|2|
|1505|priorityorder|2|
|1506|privacy|2|
|1507|processingquestion|2|
|1508|producing|2|
|1509|producta|2|
|1510|productasalesquantity|2|
|1511|productb|2|
|1512|productc|2|
|1513|prohibit|2|
|1514|projects|2|
|1515|pronounced|2|
|1516|propranolol|2|
|1517|prosperity|2|
|1518|providing|2|
|1519|psychologyquestion|2|
|1520|ptosisalready|2|
|1521|purpose|2|
|1522|puts|2|
|1523|pwave|2|
|1524|qi|2|
|1525|qiao|2|
|1526|qrscomplex|2|
|1527|qwenlm|2|
|1528|ran|2|
|1529|rapid|2|
|1530|rate|2|
|1531|rateofreturn|2|
|1532|ratio|2|
|1533|ray|2|
|1534|reach|2|
|1535|readme|2|
|1536|real|2|
|1537|reason|2|
|1538|recalling|2|
|1539|recent|2|
|1540|recently|2|
|1541|receptors|2|
|1542|referring|2|
|1543|refine|2|
|1544|refractive|2|
|1545|regular|2|
|1546|rejecttoanswer|2|
|1547|rekacore|2|
|1548|release|2|
|1549|relevant|2|
|1550|religion|2|
|1551|remedy|2|
|1552|reported|2|
|1553|represent|2|
|1554|represented|2|
|1555|representing|2|
|1556|repurposed|2|
|1557|requiring|2|
|1558|researchquestion|2|
|1559|respectively|2|
|1560|result|2|
|1561|retainedearningstobereported|2|
|1562|review|2|
|1563|revolutionizing|2|
|1564|right|2|
|1565|rightventricle|2|
|1566|rigorous|2|
|1567|robust|2|
|1568|robustness|2|
|1569|roomwithinaroom|2|
|1570|round|2|
|1571|sa|2|
|1572|sampled|2|
|1573|savingthemfrompovertyoroppressionandbringingthemtrade|2|
|1574|scene|2|
|1575|scenes|2|
|1576|scope|2|
|1577|sculpture|2|
|1578|sebastian|2|
|1579|select|2|
|1580|selective|2|
|1581|selects|2|
|1582|sequence|2|
|1583|several|2|
|1584|shade|2|
|1585|shall|2|
|1586|shao|2|
|1587|share|2|
|1588|sheet|2|
|1589|shen|2|
|1590|shijie|2|
|1591|shortcomings|2|
|1592|showingawillingnesstobecomparedtogreatspanishpaintersofthepast|2|
|1593|shuai|2|
|1594|simpedanceinthes|2|
|1595|simple|2|
|1596|simvlm|2|
|1597|sites|2|
|1598|siyuan|2|
|1599|size|2|
|1600|sketches|2|
|1601|sleg|2|
|1602|socialsci|2|
|1603|solid|2|
|1604|songyang|2|
|1605|sonnet|2|
|1606|sparkles|2|
|1607|speciesbdescendedfromspeciesa|2|
|1608|specifically|2|
|1609|springer|2|
|1610|square|2|
|1611|standardized|2|
|1612|standards|2|
|1613|startsafterafinishesandcompleteswithoutbeinginterleavedwithanyothertransaction|2|
|1614|statistical|2|
|1615|stem|2|
|1616|steps|2|
|1617|storage|2|
|1618|stored|2|
|1619|student|2|
|1620|subarachnoidspace|2|
|1621|success|2|
|1622|sufficient|2|
|1623|sustained|2|
|1624|synthesisquestion|2|
|1625|tab|2|
|1626|tackle|2|
|1627|tasked|2|
|1628|taxonomy|2|
|1629|tay|2|
|1630|technical|2|
|1631|technique|2|
|1632|testing|2|
|1633|textualunderstandingerror|2|
|1634|theartist|2|
|1635|thecabinisdepressurizedandtheoxygenmaskfallsfromtheceiling|2|
|1636|thecorrectcalculationshouldbe|2|
|1637|thedangeroflettinggoofadream|2|
|1638|thediffusionofculturaltraditionsalongeurasiantraderoutes|2|
|1639|theentranceoflightandairintothehall|2|
|1640|theextenttowhichgovernmenteconomicpoliciesineurasiaintheperiod|2|
|1641|theincidencedensity|2|
|1642|theinequitiesofsocieties|2|
|1643|thejamdensity|2|
|1644|themodel|2|
|1645|themostlikelydiagnosisis|2|
|1646|themousemaydevelopnoheadandtwotails|2|
|1647|themousemaydeveloptwoheadsandnotail|2|
|1648|thentheotherstartsbeforethefirstonefinishes|2|
|1649|theorderis|2|
|1650|theoryquestion|2|
|1651|thepatientisapost|2|
|1652|theperspectiveofthecartoonististhattheunitedstateshasbeenasaviortothenationsbroughtunderitscontrol|2|
|1653|theregionboundedbythegraphasshownabove|2|
|1654|thesearethecaseswherebothtestsarepositive|2|
|1655|thesewomenwanttheirchildrentobeeducated|2|
|1656|thespreadoftechnologicalinnovationsacrossregionsineurasia|2|
|1657|thetypeofalkylsubstituentbpresent|2|
|1658|thetypeofheterocyclicringcpresent|2|
|1659|thetypeofsubstituentaonthearomaticring|2|
|1660|theunitedstatesisseenasfulfillingwhichofthefollowingroles|2|
|1661|thevalueoftheindexis|2|
|1662|think|2|
|1663|thisconditionoftenoccursinelderlypeople|2|
|1664|thisisincorrect|2|
|1665|thisphenomenoncannotbefixedbylasik|2|
|1666|thisquestioncallsforknowledgerelatedtothestimulusmaterial|2|
|1667|thisstatementappearstobetrue|2|
|1668|thistumormayrepresentthemostcommontypeofintraocularneoplasm|2|
|1669|tiejun|2|
|1670|timelines|2|
|1671|tofind|2|
|1672|tofindthesteady|2|
|1673|took|2|
|1674|tool|2|
|1675|tot|2|
|1676|totalmanufacturingcostforproducta|2|
|1677|touvron|2|
|1678|train|2|
|1679|trainable|2|
|1680|trained|2|
|1681|transactionaoncpu|2|
|1682|transactionboncpu|2|
|1683|transformers|2|
|1684|trend|2|
|1685|truenegatives|2|
|1686|underscore|2|
|1687|underscores|2|
|1688|unit|2|
|1689|uniter|2|
|1690|universal|2|
|1691|unknown|2|
|1692|unlocking|2|
|1693|uponinspection|2|
|1694|uptodistinguishitfrommelanoma|2|
|1695|url|2|
|1696|used|2|
|1697|va|2|
|1698|variable|2|
|1699|vaultedroof|2|
|1700|vb|2|
|1701|vce|2|
|1702|vdoesn|2|
|1703|ventriculardepolarization|2|
|1704|version|2|
|1705|vfailstointerprettheimage|2|
|1706|vilbert|2|
|1707|vinvl|2|
|1708|visionmayberestoredwithconvexlensandrefractivesurgery|2|
|1709|visually|2|
|1710|vit|2|
|1711|vrecalledtherightknowledgeandmadetherightreasoning|2|
|1712|w|2|
|1713|weak|2|
|1714|web|2|
|1715|wecan|2|
|1716|wecandeduce|2|
|1717|weightedindexofthethreestocksforthefirstperiod|2|
|1718|weightedindexvalueat|2|
|1719|wenqi|2|
|1720|whatisthemostlikelydiagnosis|2|
|1721|whichisincorrect|2|
|1722|whichisnotexplicitlymarkedinthefigurebutisonlydescribedintext|2|
|1723|whichofthesepicturesshowsthereconciliationofegoismandother|2|
|1724|willbeactivatedandinhibittheseedlingtripleresponse|2|
|1725|withthehpointingtothebackground|2|
|1726|withtheswitchinposition|2|
|1727|won|2|
|1728|wouldn|2|
|1729|www|2|
|1730|xiaowei|2|
|1731|xing|2|
|1732|xiujun|2|
|1733|yacoob|2|
|1734|yao|2|
|1735|yaser|2|
|1736|years|2|
|1737|yifan|2|
|1738|ying|2|
|1739|youaretravelingonaplanewithasmallchild|2|
|1740|yuhang|2|
|1741|yuheng|2|
|1742|zhai|2|
|1743|zhe|2|
|1744|zhengyuan|2|
|1745|zhong|2|
|1746|zhuang|2|
|1747|zicheng|2|
|1748|zirui|2|
|1749|zitnick|2|
|1750|ziwei|2|
|1751|ziyi|2|
|1752|zou|2|
|合計|5,688|	20,849|

合計は出現数１の単語を含みます。

<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words and/or centences in order.

# Qiita Calendar 2024
2024 参加・主催Calendarと投稿記事一覧 Qiita(248)
https://qiita.com/kaizen_nagoya/items/d80b8fbac2496df7827f

主催Calendar2024分析 Qiita(254)
https://qiita.com/kaizen_nagoya/items/15807336d583076f70bc

Calendar 統計
https://qiita.com/kaizen_nagoya/items/e315558dcea8ee3fe43e

LLM 関連 Calendar 2024 
https://qiita.com/kaizen_nagoya/items/c36033cf66862d5496fa

Large Language Model Related Calendar 
https://qiita.com/kaizen_nagoya/items/3beb0bc3fb71e3ae6d66

博士論文 Calendar 2024 を開催します。 
https://qiita.com/kaizen_nagoya/items/51601357efbcaf1057d0

博士論文(0)関連記事一覧
https://qiita.com/kaizen_nagoya/items/8f223a760e607b705e78

# 自己記事一覧
Qiitaで逆リンクを表示しなくなったような気がする。時々、スマフォで表示するとあらわっることがあり、完全に削除したのではなさそう。

４月以降、せっせとリンクリストを作り、統計を取って確率を説明しようとしている。
2025年２月末を目標にしている。

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

C++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48) 
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

なぜdockerで機械学習するか 書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

Python(0)記事をまとめたい。
https://qiita.com/kaizen_nagoya/items/088c57d70ab6904ebb53

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

技術士(0)一覧
https://qiita.com/kaizen_nagoya/items/ce4ccf4eb9c5600b89ea

Reserchmap(0) 一覧
https://qiita.com/kaizen_nagoya/items/506c79e562f406c4257e

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学 
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

coq(0) 一覧
https://qiita.com/kaizen_nagoya/items/d22f9995cf2173bc3b13

統計(0)一覧
https://qiita.com/kaizen_nagoya/items/80d3b221807e53e88aba

図(0) state, sequence and timing. UML and お絵描き 
https://qiita.com/kaizen_nagoya/items/60440a882146aeee9e8f

色(0) 記事100書く切り口 
https://qiita.com/kaizen_nagoya/items/22331c0335ed34326b9b

品質一覧 
https://qiita.com/kaizen_nagoya/items/2b99b8e9db6d94b2e971

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

水の資料集(0)　方針と成果
https://qiita.com/kaizen_nagoya/items/f5dbb30087ea732b52aa

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

音楽　一覧(0) 
https://qiita.com/kaizen_nagoya/items/b6e5f42bbfe3bbe40f5d

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧 
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/faa4ea03d91d901a618a

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　 ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

# 関連資料
' @kazuo_reve 私が効果を確認した「小川メソッド」
https://qiita.com/kazuo_reve/items/a3ea1d9171deeccc04da

' @kazuo_reve 新人の方によく展開している有益な情報
https://qiita.com/kazuo_reve/items/d1a3f0ee48e24bba38f1

' @kazuo_reve Vモデルについて勘違いしていたと思ったこと
https://qiita.com/kazuo_reve/items/46fddb094563bd9b2e1e


## Engineering Festa 2024前に必読記事一覧
programの本質は計画だ。programは設計だ。
https://qiita.com/kaizen_nagoya/items/c8545a769c246a458c27

登壇直後版 色使い(JIS安全色) Qiita Engineer Festa 2023〜私しか得しないニッチな技術でLT〜 スライド編 0.15
https://qiita.com/kaizen_nagoya/items/f0d3070d839f4f735b2b

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

逆も真：社会人が最初に確かめるとよいこと。OSEK(69)、Ethernet(59)
https://qiita.com/kaizen_nagoya/items/39afe4a728a31b903ddc

統計の嘘。仮説（127）
https://qiita.com/kaizen_nagoya/items/63b48ecf258a3471c51b

自分の言葉だけで論理展開できるのが天才なら、文章の引用だけで論理展開できるのが秀才だ。仮説（136）
https://qiita.com/kaizen_nagoya/items/97cf07b9e24f860624dd

参考文献駆動執筆(references driven writing)・デンソークリエイト編
https://qiita.com/kaizen_nagoya/items/b27b3f58b8bf265a5cd1

「何を」よりも「誰を」。１０年後のために今見習いたい人たち
https://qiita.com/kaizen_nagoya/items/8045978b16eb49d572b2

Qiitaの記事に３段階または５段階で到達するための方法
https://qiita.com/kaizen_nagoya/items/6e9298296852325adc5e

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

あなたは「勘違いまとめ」から、勘違いだと言っていることが勘違いだといくつ見つけられますか。人間の間違い(human error(125))の種類と対策
https://qiita.com/kaizen_nagoya/items/ae391b77fffb098b8fb4

プログラマの「プログラムが書ける」思い込みは強みだ。３つの理由。仮説（168）統計と確率(17) , OSEK(79)
https://qiita.com/kaizen_nagoya/items/bc5dd86e414de402ec29

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

これからの情報伝達手段の在り方について考えてみよう。炎上と便乗。
https://qiita.com/kaizen_nagoya/items/71a09077ac195214f0db

ISO/IEC JTC1 SC7 Software and System Engineering
https://qiita.com/kaizen_nagoya/items/48b43f0f6976a078d907

アクセシビリティの知見を発信しよう！（再び）
https://qiita.com/kaizen_nagoya/items/03457eb9ee74105ee618

統計論及確率論輪講（再び）
https://qiita.com/kaizen_nagoya/items/590874ccfca988e85ea3

読者の心をグッと惹き寄せる7つの魔法
https://qiita.com/kaizen_nagoya/items/b1b5e89bd5c0a211d862

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

ソースコードで議論しよう。日本語で議論するの止めましょう（あるプログラミング技術の議論報告）
https://qiita.com/kaizen_nagoya/items/8b9811c80f3338c6c0b0

脳内コンパイラの3つの危険
https://qiita.com/kaizen_nagoya/items/7025cf2d7bd9f276e382

心理学の本を読むよりはコンパイラ書いた方がよくね。仮説（34）
https://qiita.com/kaizen_nagoya/items/fa715732cc148e48880e

NASAを超えるつもりがあれば読んでください。
https://qiita.com/kaizen_nagoya/items/e81669f9cb53109157f6

データサイエンティストの気づき!「勉強して仕事に役立てない人。大嫌い!!」『それ自分かも?』ってなった!!! 
https://qiita.com/kaizen_nagoya/items/d85830d58d8dd7f71d07

「ぼくの好きな先生」「人がやらないことをやれ」プログラマになるまで。仮説（37）　
https://qiita.com/kaizen_nagoya/items/53e4bded9fe5f724b3c4

なぜ経済学徒を辞め、計算機屋になったか（経済学部入学前・入学後・卒業後対応） 転職(1)  
https://qiita.com/kaizen_nagoya/items/06335a1d24c099733f64    

プログラミング言語教育のXYZ。 仮説（52） 
https://qiita.com/kaizen_nagoya/items/1950c5810fb5c0b07be4

【24卒向け】9ヶ月後に年収１０００万円を目指す。二つの関門と三つの道。
https://qiita.com/kaizen_nagoya/items/fb5bff147193f726ad25

「【25卒向け】Qiita Career Meetup for STUDENT」予習の勧め
https://qiita.com/kaizen_nagoya/items/00eadb8a6e738cb6336f

大学入試不合格でも筆記試験のない大学に入って卒業できる。卒業しなくても博士になれる。
https://qiita.com/kaizen_nagoya/items/74adec99f396d64b5fd5

全世界の不登校の子供たち「博士論文」を書こう。世界子供博士論文遠隔実践中心 安全(99)
https://qiita.com/kaizen_nagoya/items/912d69032c012bcc84f2

小川メソッド　覚え（書きかけ）
https://qiita.com/kaizen_nagoya/items/3593d72eca551742df68

DoCAP（ドゥーキャップ）って何ですか？
https://qiita.com/kaizen_nagoya/items/47e0e6509ab792c43327

views 20,000越え自己記事一覧
https://qiita.com/kaizen_nagoya/items/58e8bd6450957cdecd81

Views１万越え、もうすぐ１万記事一覧 最近いいねをいただいた213記事
https://qiita.com/kaizen_nagoya/items/d2b805717a92459ce853

amazon 殿堂入りNo1レビュアになるまで。仮説(102)
https://qiita.com/kaizen_nagoya/items/83259d18921ce75a91f4

100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.
### 文書履歴(document history)
ver. 0.01 初稿 　20241128
### 最後までおよみいただきありがとうございました。
いいね　💚、フォローをお願いします。
#### Thank you very much for reading to the last sentence.
Please press the like icon 💚　and follow me for your happy life.