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As practitioners seek to surpass the current reliability and quality frontier of monolithic models, Compound AI Systems consisting of many language model inference calls are increasingly employed.
A method for obtaining digital signatures and public-key cryptosystems
R. L. Rivest, A. Shamir, and L. Adleman · 1978
Earlier work this paper cites.
The complexity of probabilistic verification
Costas Courcoubetis and Mihalis Yannakakis · 1995
Earlier work this paper cites.
Model checking
Edmund M Clarke · 1997
Earlier work this paper cites.
Probabilistic checking of proofs: a new characterization of np
Sanjeev Arora and Shmuel Safra · 1998
Earlier work this paper cites.
Computational complexity
Christos H Papadimitriou · 2003
Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
Are more llm calls all you need? towards scaling laws of compound inference systems
Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei Zaharia, and James Zou · 2024
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Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Mixture-of-agents enhances large language model capabilities
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Can large language models really improve by self-critiquing their own plans?
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati · 2023
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URL https://api.semanticscholar.org/CorpusID:266058988
Alphacode 2 technical report
Cited in the paper.
URL https://ai.meta.com/blog/meta-llama-3/
Introducing meta llama 3: The most capable openly available llm to date
Cited in the paper.
URL https://www.anthropic.com/news/claude-3-family
Introducing the next generation of claude
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Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou · 2024
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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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