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There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, August 2019
Nils Reimers and Iryna Gurevych · 1908
Earlier work this paper cites.
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, May 2020
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 1910
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library, December 2019
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Killian Weinberger · 2015
Earlier work this paper cites.
Training Deep Nets with Sublinear Memory Cost, April 2016
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization, January 2017
Diederik P. Kingma and Jimmy Ba · 2017
Earlier work this paper cites.
SGDR: Stochastic Gradient Descent with Warm Restarts, May 2017
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
FarmBeats: An IoT platform for data-driven agriculture
Deepak Vasisht, Zerina Kapetanovic, Jongho Won, Xinxin Jin, Ranveer Chandra, Sudipta Sinha, Ashish Kapoor, Madhusudhan Sudarshan, and Sean Stratman · 2017
Earlier work this paper cites.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, April 2018
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2018
Earlier work this paper cites.
Mixed Precision Training, February 2018
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2018
Earlier work this paper cites.
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Earlier work this paper cites.
Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Yuxiang Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Micro-climate prediction-multi scale encoder-decoder based deep learning framework
Peeyush Kumar, Ranveer Chandra, Chetan Bansal, Shivkumar Kalyanaraman, Tanuja Ganu, and Michael Grant · 2021
Earlier work this paper cites.
Farmvibes: Precision agriculture with ai, 2021
Microsoft · 2021
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Democratizing data-driven agriculture using affordable hardware
Ranveer Chandra, Swami Manohar, Tusher Chakraborty, Jian Ding, Zerina Kapetanovic, Peeyush Kumar, and Deepak Vasisht · 2022
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Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, and Yinfei Yang · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe · 2022
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Microsoft copilot impact on finance, 2022
Vena Solutions · 2022
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https://github.com/kermitt2/grobid , 2008–2023
A deep learning–based approach for predicting anticancer drug response using gene expression profiles
J. Kim, S. Lee, S. Lee, and M. Kim · 2023
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Large language models for supply chain optimization
Beibin Li, Konstantina Mellou, Bo Zhang, Jeevan Pathuri, and Ishai Menache · 2023
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Azureml metrics python package, 2023
Microsoft · 2023
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Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2023
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Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz · 2023
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Grobid · 2023
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https://github.com/guidance-ai/guidance/tree/main , 2023
Guidance framework · 2023
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Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy · 2023
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Revolutionizing healthcare: the role of artificial intelligence in clinical practice
S.A. Alowais, S.S. Alghamdi, N. Alsuhebany, et al · 2023
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Beautifulsoup, 2023
BeautifulSoup · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
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Can Large Language Models Be an Alternative to Human Evaluations?, May 2023
Cheng-Han Chiang and Hung-yi Lee · 2023
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OpenAI · 2023
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Pdf2text, 2023
PDF2Text · 2023
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Pypdf, 2023
PyPDF · 2023
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Knowledge guided representation learning and causal structure learning in soil science, 2023
Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, and Ranveer Chandra · 2023
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GPT-4 as an agronomist assistant? answering agriculture exams using large language models
Bruno Silva, Leonardo Nunes, VIjay Estevão, Robertp amd Aski, and Ranveer Chandra · 2023
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Large language models in medicine
A J Thirunavukarasu, D S J Ting, K Elangovan, et al · 2023
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How llm applications are revolutionizing the manufacturing industry, 2023
Vanti · 2023
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Seeing through clouds in satellite images
Mingmin Zhao, Peder Olsen, and Ranveer Chandra · 2023
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena, October 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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JudgeLM: Fine-tuned Large Language Models are Scalable Judges, October 2023
Lianghui Zhu, Xinggang Wang, and Xinlong Wang · 2023
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Scrapy, 2023
Zyte · 2023
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