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The ability of Large Language Models (LLMs) to generate structured outputs, such as JSON, is crucial for their use in Compound AI Systems.
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, Sebastian Riedel, and Douwe Kiela · 2020
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Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez · 2023
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Yaniv Leviathan, Matan Kalman, and Yossi Matias · 2023
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Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
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The shift from models to compound ai systems
Matei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, and Ali Ghodsi · 2024
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Alto: An efficient network orchestrator for compound ai systems
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Luca Beurer-Kellner, Marc Fischer, and Martin Vechev · 2024
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Optimizing instructions and demonstrations for multi-stage language model programs
Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, and Omar Khattab · 2024
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Dspy assertions: Computational constraints for self-refining language model pipelines
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Networks of networks: Complexity class principles applied to compound ai systems design
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