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There is a rapidly growing number of large language models (LLMs) that users can query for a fee.
Stochastic gradient boosting
Jerome H Friedman · 2002
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Robust real-time face detection
Paul Viola and Michael J Jones · 2004
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Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
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A cascade ranking model for efficient ranked retrieval
Lidan Wang, Jimmy Lin, and Donald Metzler · 2011
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Exploring the diversity in cluster ensemble generation: Random sampling and random projection
Fan Yang, Xuan Li, Qianmu Li, and Tao Li · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
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Weakly supervised cascaded convolutional networks
Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash, and Luc Van Gool · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Taking ai to the edge: Google’s tpu now comes in a maker-friendly package
Stephen Cass · 2019
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Beyond data and model parallelism for deep neural networks
Zhihao Jia, Matei Zaharia, and Alex Aiken · 2019
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Frugalml: How to use ml prediction apis more accurately and cheaply
Lingjiao Chen, Matei Zaharia, and James Y Zou · 2020
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Racial disparities in automated speech recognition
Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
Cited alongside, same era.
Did the model change? efficiently assessing machine learning api shifts
Lingjiao Chen, Tracy Cai, Matei Zaharia, and James Zou · 2021
Cited alongside, same era.
Generated knowledge prompting for commonsense reasoning
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi · 2021
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
Cited alongside, same era.
Terapipe: Token-level pipeline parallelism for training large-scale language models
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickael Seznec, Julien Demouth, and Song Han · 2022
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, and Ed Chi · 2022
Later among the works it cites.
https://www.ai21.com/
AI21 LLM API · 2023
Closest in time.
https://openai.com/blog/chatgpt
ChatGPT Announcement · 2023
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Zhuohan Li, Siyuan Zhuang, Shiyuan Guo, Danyang Zhuo, Hao Zhang, Dawn Song, and Ion Stoica · 2021
Cited alongside, same era.
Impact of news on the commodity market: Dataset and results
Ankur Sinha and Tanmay Khandait · 2021
Cited alongside, same era.
Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021
Ben Wang and Aran Komatsuzaki · 2021
Cited alongside, same era.
When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings
Lucia Zheng, Neel Guha, Brandon R Anderson, Peter Henderson, and Daniel E Ho · 2021
Cited alongside, same era.
Ask me anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel J Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré · 2022
Cited alongside, same era.
Towards efficient post-training quantization of pre-trained language models
Haoli Bai, Lu Hou, Lifeng Shang, Xin Jiang, Irwin King, and Michael R Lyu · 2022
Cited alongside, same era.
Efficient online ml api selection for multi-label classification tasks
Lingjiao Chen, Matei Zaharia, and James Zou · 2022
Cited alongside, same era.
Successive prompting for decomposing complex questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner · 2022
Cited alongside, same era.
https://cohere.com/
CoHere LLM API · 2023
Closest in time.
https://www.semianalysis.com/p/the-inference-cost-of-search-disruption
Cost estimation of using GPT-3 for real applications · 2023
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https://neoteric.eu/blog/how-much-does-it-cost-to-use-gpt-models-gpt-3-pricing-explained
Cost estimation of using GPT-3 for real applications · 2023
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https://beta.forefront.ai/
forefront AI LLM API · 2023
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Ziplm: Hardware-aware structured pruning of language models
Eldar Kurtic, Elias Frantar, and Dan Alistarh · 2023
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Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al · 2023
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https://platform.openai.com/
OpenAI LLM API · 2023
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OpenAI · 2023
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https://textsynth.com/
Textsynth LLM API · 2023
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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
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A comprehensive study on post-training quantization for large language models
Zhewei Yao, Cheng Li, Xiaoxia Wu, Stephen Youn, and Yuxiong He · 2023
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