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Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems.
Reading, Massachusetts: Addison-Wesley, 1st ed., 1975
F. P. B. Jr., The Mythical Man-Month: Essays on Software Engineering · 1975
Earlier work this paper cites.
USA: Wiley-Interscience, 1975
L. Kleinrock, Theory, Volume 1, Queueing Systems · 1975
Earlier work this paper cites.
G. C. Necula, “Proof-carrying code,” in POPL ’97: Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
1997
Earlier work this paper cites.
https://dl.acm.org/doi/10.1145/1459352.1459354
R. M. Hierons, K. Bogdanov, J. P. Bowen, R. Cleaveland, J. Derrick, J. Dick, M. Gheorghe, M. Harman, K. Kapoor, P. Krause, G. Lüttgen, A. J. H. Simons, S. Vilkomir, M. R. Woodward, and H. Zedan, “Using formal specifications to support testing,” 2009 · 2009
Earlier work this paper cites.
https://a16z.com/why-software-is-eating-the-world/
M. Andreessen, “Why software is eating the world,” 2011 · 2011
Earlier work this paper cites.
Graduate Texts in Mathematics, Springer New York, 2011
E. Çınlar, Probability and Stochastics · 2011
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” 2016
2016
Earlier work this paper cites.
https://www.technologyreview.com/2017/05/12/151722/nvidia-ceo-software-is-eating-the-world-but-ai-is-going-to-eat-software
T. Simonite, “Nvidia ceo: Software is eating the world, but ai is going to eat software,” 2017 · 2017
Earlier work this paper cites.
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, and D. Hassabis, “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” 2017
2017
Earlier work this paper cites.
L. Chen, M. Zaharia, and J. Zou, “Frugalml: How to use ml prediction apis more accurately and cheaply,” 2020
2020
Earlier work this paper cites.
P. Tolmach, Y. Li, S.-W. Lin, Y. Liu, and Z. Li, “A survey of smart contract formal specification and verification,” 2021
2021
Earlier work this paper cites.
M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, C. Sutton, and A. Odena, “Show your work: Scratchpads for intermediate computation with language models,” 2021
2021
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” 2021
2021
Earlier work this paper cites.
X. Zhu, S. Wen, S. Camtepe, and Y. Xiang, “Fuzzing: A survey for roadmap,” ACM Comput. Surv
2022
Earlier work this paper cites.
W. B. Demilie and F. G. Deriba, “Detection and prevention of sqli attacks and developing compressive framework using machine learning and hybrid techniques,” Journal of Big Data
2022
Earlier work this paper cites.
Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li, E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite, L. Lovitt, M. Sellitto, N. Elhage, N. Schiefer, N. Mercado, N. DasSarma, R. Lasenby, R. Larson, S. Ringer, S. Johnston, S. Kravec, S. E. Showk, S. Fort, T. Lanham, T. Telleen-Lawton, T. Conerly, T. Henighan, T. Hume, S. R. Bowman, Z. Hatfield-Dodds, B. Mann, D. Amodei, N. Joseph, S. McCandlish, T. Brown, and J. Kaplan, “Constitutional ai: Harmlessness from ai feedback,” 2022
2022
Earlier work this paper cites.
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago, T. Hubert, P. Choy, C. de Masson d’Autume, I. Babuschkin, X. Chen, P.-S. Huang, J. Welbl, S. Gowal, A. Cherepanov, J. Molloy, D. J. Mankowitz, E. Sutherland Robson, P. Kohli, N. de Freitas, K. Kavukcuoglu, and O. Vinyals, “Competition-level code generation with alphacode,” Science
2022
Earlier work this paper cites.
https://openreview.net/pdf?id=BZ5a1r-kVsf
Y. LeCunn, “A path towards autonomous machine intelligence,” 2022 · 2022
Earlier work this paper cites.
https://www.linkedin.com/pulse/ai-eating-software-world-kumar-aakash-aacash-eth-/
A. Kumar, “Ai is eating the software that is eating the world,” 2023 · 2023
Earlier work this paper cites.
https://danielmiessler.com/p/ai-is-eating-the-software-world/
D. Miessler, “How ai is eating the software world,” 2023 · 2023
Earlier work this paper cites.
https://www.amjmed.com/action/showPdf?pii=S0002-9343%2823%2900401-1
Jerome Goddard, “Hallucinations in ChatGPT: A cautionary tale for biomedical researchers,” in The American Journal of Medicine · 2023
Cited alongside, same era.
O. Khattab, A. Singhvi, P. Maheshwari, Z. Zhang, K. Santhanam, S. Vardhamanan, S. Haq, A. Sharma, T. T. Joshi, H. Moazam, H. Miller, M. Zaharia, and C. Potts, “Dspy: Compiling declarative language model calls into self-improving pipelines,” 2023
2023
Cited alongside, same era.
B. T. Willard and R. Louf, “Efficient guided generation for large language models,” 2023
2023
Cited alongside, same era.
S. K. Lahiri, S. Fakhoury, A. Naik, G. Sakkas, S. Chakraborty, M. Musuvathi, P. Choudhury, C. von Veh, J. P. Inala, C. Wang, and J. Gao, “Interactive code generation via test-driven user-intent formalization,” 2023
2023
Cited alongside, same era.
https://www.ben-morris.com/why-building-ai-powered-agents-is-so-challenging-for-now/
B. Morris, “Why building ai-powered agents is so challenging. for now.,” 2024 · 2024
Closest in time.
H. Luo and L. Specia, “From understanding to utilization: A survey on explainability for large language models,” 2024
2024
Closest in time.
https://github.com/openai/transformer-debugger
“Transformer debugger,” 2024 · 2024
Closest in time.
https://github.com/ruizheliUOA/Awesome-Interpretability-in-Large-Language-Models
“Awesome interpretability in large language models,” 2024 · 2024
Closest in time.
https://docs.google.com/document/d/1WZwS4ZAWFNTwusFmW6UKYrs9QqYy7nc2qLPZbeuCmDE/edit
K. Goldberg, “Is data all you need?: Large robot action models and good old fashioned engineering,” 2024 · 2024
Closest in time.
https://lamport.azurewebsites.net/tla/tla.html
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” 2023
2023
Cited alongside, same era.
J. Lanchantin, S. Toshniwal, J. Weston, A. Szlam, and S. Sukhbaatar, “Learning to reason and memorize with self-notes,” 2023
2023
Cited alongside, same era.
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, and I. Stoica, “Judging llm-as-a-judge with mt-bench and chatbot arena,” 2023
2023
Cited alongside, same era.
H. Yang, S. Yue, and Y. He, “Auto-gpt for online decision making: Benchmarks and additional opinions,” 2023
2023
Cited alongside, same era.
L. Chen, M. Zaharia, and J. Zou, “Frugalgpt: How to use large language models while reducing cost and improving performance,” 2023
2023
Cited alongside, same era.
Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, “Autogen: Enabling next-gen llm applications via multi-agent conversation,” 2023
2023
Cited alongside, same era.
Y. Tian, W. Yan, Q. Yang, X. Zhao, Q. Chen, W. Wang, Z. Luo, L. Ma, and D. Song, “Codehalu: Investigating code hallucinations in llms via execution-based verification,” 2024
2024
Cited alongside, same era.
L. Lamport, “TLA+,” 2024 · 2024
Closest in time.
M. Endres, S. Fakhoury, S. Chakraborty, and S. K. Lahiri, “Can large language models transform natural language intent into formal method postconditions?,” 2024
2024
Closest in time.
S. K. Lahiri, “Evaluating llm-driven user-intent formalization for verification-aware languages,” 2024
2024
Closest in time.
https://arxiv.org/html/2401.02500v2
V. Pallagani, K. Roy, B. Muppasani, F. Fabiano, A. Loreggia, K. Murugesan, B. Srivastava1, F. Rossi, L. Horesh, and A. Sheth1, “On the prospects of incorporating large language models (LLMs) in automated planning and scheduling (APS),” 2024 · 2024
Closest in time.
L. Luo, Y. Liu, R. Liu, S. Phatale, H. Lara, Y. Li, L. Shu, Y. Zhu, L. Meng, J. Sun, and A. Rastogi, “Improve mathematical reasoning in language models by automated process supervision,” 2024
2024
Closest in time.
2024
Closest in time.
W.-L. Chiang, L. Zheng, Y. Sheng, A. N. Angelopoulos, T. Li, D. Li, H. Zhang, B. Zhu, M. Jordan, J. E. Gonzalez, and I. Stoica, “Chatbot arena: An open platform for evaluating llms by human preference,” 2024
2024
Closest in time.
T. Ridnik, D. Kredo, and I. Friedman, “Code generation with alphacodium: From prompt engineering to flow engineering,” 2024
2024
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J. Q. Davis, B. Hanin, L. Chen, P. Bailis, I. Stoica, and M. Zaharia, “Networks of networks: Complexity class principles applied to compound ai systems design,” 2024
2024
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P. Verga, S. Hofstatter, S. Althammer, Y. Su, A. Piktus, A. Arkhangorodsky, M. Xu, N. White, and P. Lewis, “Replacing judges with juries: Evaluating llm generations with a panel of diverse models,” 2024
2024
Closest in time.
C. Packer, S. Wooders, K. Lin, V. Fang, S. G. Patil, I. Stoica, and J. E. Gonzalez, “Memgpt: Towards llms as operating systems,” 2024
2024
Closest in time.
I. Ong, A. Almahairi, V. Wu, W.-L. Chiang, T. Wu, J. E. Gonzalez, M. W. Kadous, and I. Stoica, “Routellm: Learning to route llms with preference data,” 2024
2024
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“Mixture-of-agents enhances large language model capabilities,” 2024
2024
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T. H. Trinh, Y. Wu, Q. V. Le, H. He, and T. Luong, “Solving olympiad geometry without human demonstrations,” 2024
2024
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