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The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks.
The Penn Treebank: Annotating predicate argument structure
Mitch Marcus, P.Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Ferguson M., et al · 1994
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard & Socher · 2016
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A diagram is worth a dozen images, 2016
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, et al · 2017
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, et al · 2018
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Think you have solved question answering? Try ARC-DA, the AI2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, et al · 2018
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HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin & Choi · 2019
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, et al · 2020
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PIQA: Reasoning about physical commonsense in natural language
Y Bisk, R Zellers, R Le Bras, J Gao, and Y & Choi · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin & Choi · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, et al · 2021
Cited alongside, same era.
GPTQ: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan & Alistarh · 2022
Cited alongside, same era.
Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
Cited alongside, same era.
LLaMA: open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, et al · 2023
PB-LLM: Partially binarized large language models
Yuzhang Shang, Zhihang Yuan, Qiang Wu, and Zhen & Dong · 2024
Closest in time.
QuIP: 2-bit quantization of large language models with guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, and Christopher & De Sa · 2024
Closest in time.
BiLLM: Pushing the limit of post-training quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin, Ying Li, Shiming Zhang, Xianglong Liu, et al · 2024
Closest in time.
QLoRA: Efficient finetuning of quantized LLMs
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke & Zettlemoyer · 2024
Closest in time.
Accurate lora-finetuning quantization of LLMs via information retention
Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, et al · 2024
Closest in time.
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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Cited alongside, same era.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae & Lee · 2023
Cited alongside, same era.
SmoothQuant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song & Han · 2023
Cited alongside, same era.
QuantSR: accurate low-bit quantization for efficient image super-resolution
Haotong Qin, Yulun Zhang, Yifu Ding, Xianglong Liu, Martin Danelljan, Fisher Yu, et al · 2023
Cited alongside, same era.
Omniquant: Omnidirectionally calibrated quantization for large language models
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo · 2023
Cited alongside, same era.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, et al · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Cited alongside, same era.
SliM-LLM: Salience-driven mixed-precision quantization for large language models
Wei Huang, Haotong Qin, Yangdong Liu, Yawei Li, Xianglong Liu, Luca Benini, et al · 2024
Cited alongside, same era.
Closest in time.
LLM-QAT: Data-free quantization aware training for large language models
Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, et al · 2024
Closest in time.
I-llm: Efficient integer-only inference for fully-quantized low-bit large language models
Xing Hu, Yuan Cheng, Dawei Yang, Zhihang Yuan, Jiangyong Yu, Chen Xu, and Sifan Zhou · 2024
Closest in time.
Spinquant–llm quantization with learned rotations
Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort · 2024
Closest in time.
QA-LoRA: Quantization-aware low-rank adaptation of large language models
Yuhui Xu, Lingxi Xie, Xiaotao Gu, Xin Chen, Heng Chang, Hengheng Zhang, et al · 2024
Closest in time.
VILA: On pre-training for visual language models
Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song & Han · 2024
Closest in time.
Mmbench: Is your multi-modal model an all-around player?
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al · 2025
Closest in time.