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Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages.
Ethnologue: Languages of the World
Melvyn Lewis · 2009
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Neural text summarization: A critical evaluation, 2019
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Understanding guided image captioning performance across domains
Edwin G. Ng, Bo Pang, Piyush Sharma, and Radu Soricut · 2020
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Visually grounded reasoning across languages and cultures
Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti, Siva Reddy, Nigel Collier, and Desmond Elliott · 2021
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xgqa: Cross-lingual visual question answering
Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin O Steitz, Stefan Roth, Ivan Vulić, and Iryna Gurevych · 2021
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Broaden the vision: Geo-diverse visual commonsense reasoning
Da Yin, Liunian Harold Li, Ziniu Hu, Nanyun Peng, and Kai-Wei Chang · 2021
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Maxm: Towards multilingual visual question answering
Soravit Changpinyo, Linting Xue, Idan Szpektor, Ashish V Thapliyal, Julien Amelot, Michal Yarom, Xi Chen, and Radu Soricut · 2022
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No language left behind: Scaling human-centered machine translation
Marta R Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, et al · 2022
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World values survey: Round seven-country-pooled datafile version 3.0. jd systems institute & wvsa secretariat, 2022
C Haerpfer, R Inglehart, A Moreno, C Welzel, K Kizilova, J Diez-Medrano, M Lagos, P Norris, E Ponarin, and B Puranen · 2022
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Glottolog database 4.6, 2022
Harald Hammarström, Robert Forkel, Martin Haspelmath, and Sebastian Bank · 2022
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A survey of evaluation metrics used for nlg systems
Ananya B Sai, Akash Kumar Mohankumar, and Mitesh M Khapra · 2022
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Persianllama: Towards building first persian large language model
Mohammad Amin Abbasi, Arash Ghafouri, Mahdi Firouzmandi, Hassan Naderi, and Behrouz Minaei Bidgoli · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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Seed-bench: Benchmarking multimodal llms with generative comprehension
Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan · 2023
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Taiwan llm: Bridging the linguistic divide with a culturally aligned language model
Yen-Ting Lin and Yun-Nung Chen · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji · 2024
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Chatglm: A family of large language models from glm-130b to glm-4 all tools
Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, et al · 2024
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Arabicmmlu: Assessing massive multitask language understanding in arabic
Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Boda Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, et al · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2024
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Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al · 2023
Cited alongside, same era.
Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2023
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M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models
Wenxuan Zhang, Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing · 2023
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Towards measuring and modeling" culture" in llms: A survey
Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O’Neill, Ashutosh Modi, and Monojit Choudhury · 2024
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Peacock: A family of arabic multimodal large language models and benchmarks
Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, and Muhammad Abdul-Mageed · 2024
Cited alongside, same era.
Transfer q star: Principled decoding for llm alignment, 2024
Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha, Mengdi Wang, Amrit Singh Bedi, and Furong Huang · 2024
Cited alongside, same era.
Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Iliyanov Dimitrov, Ivan Koychev, and Preslav Nakov · 2024
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Benchmarking vision language models for cultural understanding
Shravan Nayak, Kanishk Jain, Rabiul Awal, Siva Reddy, Sjoerd van Steenkiste, Lisa Anne Hendricks, Karolina Stańczak, and Aishwarya Agrawal · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Glamm: Pixel grounding large multimodal model
Hanoona Rasheed, Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang, and Fahad S. Khan · 2024
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Cvqa: Culturally-diverse multilingual visual question answering benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo, Teresa Lynn, Injy Hamed, Aditya Nanda Kishore, Aishik Mandal, Alina Dragonetti, Artem Abzaliev, Atnafu Lambebo Tonja, et al · 2024
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X-former: Unifying contrastive and reconstruction learning for mllms
Swetha Sirnam, Jinyu Yang, Tal Neiman, Mamshad Nayeem Rizve, Son Tran, Benjamin Yao, Trishul Chilimbi, and Mubarak Shah · 2024
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Parrot: Multilingual visual instruction tuning
Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, et al · 2024
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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, et al · 2024
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Discover and mitigate multiple biased subgroups in image classifiers
Zeliang Zhang, Mingqian Feng, Zhiheng Li, and Chenliang Xu · 2024
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Sb-bench: Stereotype bias benchmark for large multimodal models
Vishal Narnaware, Ashmal Vayani, Rohit Gupta, Sirnam Swetha, and Mubarak Shah · 2025
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