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Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning.
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Claude Elwood Shannon · 1948
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Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Multi-agent interaction processes: From oligopoly theory to decentralized artificial intelligence
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
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The architecture of cooperation: How code architecture mitigates free riding in the open source development model
Carliss Y Baldwin and Kim B Clark · 2003
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Formal linguistic models and knowledge processing. a structuralist approach to rule-based ontology learning and population
Maria Pia Di Buono · 2016
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Attention is all you need
A Vaswani · 2017
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Publicly available clinical bert embeddings
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Activity-based analysis of open source software contributors: Roles and dynamics
Jinghui Cheng and Jin LC Guo · 2019
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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Speechmoe: Scaling to large acoustic models with dynamic routing mixture of experts
Zhao You, Shulin Feng, Dan Su, and Dong Yu · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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The landscape and gaps in open source fairness toolkits
Michelle Seng Ah Lee and Jat Singh · 2021
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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
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Neurolinguistic Programming in Clinical Settings
Lisa de Rijk, Frank Bourke, and Richard Gray · 2022
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Analysis of model compression using knowledge distillation
Yu-Wei Hong, Jenq-Shiou Leu, Muhamad Faisal, and Setya Widyawan Prakosa · 2022
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Bloom: A 176b-parameter open-access multilingual language model
BigScience Workshop, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, et al · 2022
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
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Open, closed, or small language models for text classification?
Hao Yu, Zachary Yang, Kellin Pelrine, Jean Francois Godbout, and Reihaneh Rabbany · 2023
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Elizabeth Seger, Noemi Dreksler, Richard Moulange, Emily Dardaman, Jonas Schuett, K Wei, Christoph Winter, Mackenzie Arnold, Seán Ó hÉigeartaigh, Anton Korinek, et al · 2023
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Democratizing llms: An exploration of cost-performance trade-offs in self-refined open-source models
Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai, Zhenhailong Wang, and Heng Ji · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Ren Lu, Thomas Mesnard, Johan Ferret, Colton Bishop, Ethan Hall, Victor Carbune, and Abhinav Rastogi · 2023
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From sparse to soft mixtures of experts
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, and Neil Houlsby · 2023
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Olmoe: Open mixture-of-experts language models
Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, et al · 2024
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Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al · 2024
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Rethinking data selection for supervised fine-tuning
Ming Shen · 2024
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Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, et al · 2024
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Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Cited alongside, same era.
Efficient large language models: A survey
Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, et al · 2023
Cited alongside, same era.
Alpagasus: Training a better alpaca with fewer data
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, et al · 2023
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Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models
Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Randy Zhong, Juntong Song, and Tong Zhang · 2023
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Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai · 2023
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Openchat: Advancing open-source language models with mixed-quality data
Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, and Yang Liu · 2023
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Opening up chatgpt: Tracking openness, transparency, and accountability in instruction-tuned text generators
Andreas Liesenfeld, Alianda Lopez, and Mark Dingemanse · 2023
Cited alongside, same era.
Venkatesh Balavadhani Parthasarathy, Ahtsham Zafar, Aafaq Khan, and Arsalan Shahid · 2024
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State of what art? a call for multi-prompt llm evaluation
Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, and Gabriel Stanovsky · 2024
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Studying llm performance on closed-and open-source data
Toufique Ahmed, Christian Bird, Premkumar Devanbu, and Saikat Chakraborty · 2024
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A comparative study on reasoning patterns of openai’s o1 model
Siwei Wu, Zhongyuan Peng, Xinrun Du, Tuney Zheng, Minghao Liu, Jialong Wu, Jiachen Ma, Yizhi Li, Jian Yang, Wangchunshu Zhou, et al · 2024
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Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source llms
Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, and Ondřej Dušek · 2024
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Nvlm: Open frontier-class multimodal llms
Wenliang Dai, Nayeon Lee, Boxin Wang, Zhuolin Yang, Zihan Liu, Jon Barker, Tuomas Rintamaki, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping · 2024
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Parameter-efficient fine-tuning for large models: A comprehensive survey
Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang · 2024
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Reproscreener: Leveraging llms for assessing computational reproducibility of machine learning pipelines
Adhithya Bhaskar and Victoria Stodden · 2024
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Rethinking interpretability in the era of large language models
Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, and Jianfeng Gao · 2024
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Ai as a constituted system: accountability lessons from an llm experiment
Kelsie Nabben · 2024
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Auditing large language models: a three-layered approach
Jakob Mökander, Jonas Schuett, Hannah Rose Kirk, and Luciano Floridi · 2024
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Into uncharted waters: Trade secrets law in the ai era
Burcu Kilic · 2024
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The ethics of chatgpt in medicine and healthcare: a systematic review on large language models (llms)
Joschka Haltaufderheide and Robert Ranisch · 2024
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From melting pots to misrepresentations: Exploring harms in generative ai
Sanjana Gautam, Pranav Narayanan Venkit, and Sourojit Ghosh · 2024
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Demystifying issues, causes and solutions in llm open-source projects
Yangxiao Cai, Peng Liang, Yifei Wang, Zengyang Li, and Mojtaba Shahin · 2024
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Paul Kronlund-Drouault · 2024
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Aya model: An instruction finetuned open-access multilingual language model
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, Wei-Yin Ko, Daniel D’souza, Gbemileke Onilude, Neel Bhandari, Shivalika Singh, Hui-Lee Ooi, Amr Kayid, et al · 2024
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How do hugging face models document datasets, bias, and licenses? an empirical study
Federica Pepe, Vittoria Nardone, Antonio Mastropaolo, Gabriele Bavota, Gerardo Canfora, and Massimiliano Di Penta · 2024
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A systematic comparison between open-and closed-source large language models in the context of generating gdpr-compliant data categories for processing activity records
Magdalena von Schwerin and Manfred Reichert · 2024
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Chinese firm’s large language model makes a splash
Dennis Normile · 2025
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