Fetching the paper…
Reading the bibliography…
Prompt engineering is crucial for deploying LLMs but is poorly understood mathematically.
W. L. Taylor, ““cloze procedure”: A new tool for measuring readability,” Journalism Quarterly
1953
Earlier work this paper cites.
McGraw-Hill New York, 1969
R. E. Kalman, P. L. Falb, and M. A. Arbib, Topics in mathematical system theory · 1969
Earlier work this paper cites.
T.-M. Yi, Y. Huang, M. I. Simon, and J. Doyle, “Robust perfect adaptation in bacterial chemotaxis through integral feedback control,” Proceedings of the National Academy of Sciences
2000
Earlier work this paper cites.
F.-L. Lian, J. Moyne, and D. Tilbury, “Network design consideration for distributed control systems,” IEEE transactions on control systems technology
2002
Earlier work this paper cites.
S. Roy, Y. Wan, and A. Saberi, “A network control theory approach to virus spread mitigation,” in 2009 IEEE Conference on Technologies for Homeland Security
2009
Earlier work this paper cites.
Prentice Hall, 2010
K. Ogata, Modern control engineering fifth edition · 2010
Earlier work this paper cites.
Springer, 2011
S. Aniţa, V. Arnăutu, V. Capasso, and V. Capasso, An introduction to optimal control problems in life sciences and economics: From mathematical models to numerical simulation with MATLAB® · 2011
Earlier work this paper cites.
Springer Science & Business Media, 2013
E. D. Sontag, Mathematical control theory: deterministic finite dimensional systems · 2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2015
2015
Earlier work this paper cites.
S. Merity, C. Xiong, J. Bradbury, and R. Socher, “Pointer sentinel mixture models,” 2016
2016
Earlier work this paper cites.
J. Weston, A. Bordes, S. Chopra, and T. Mikolov, “Towards ai-complete question answering: A set of prerequisite toy tasks,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings
2016
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems
2020
Earlier work this paper cites.
Z. Jiang, F. F. Xu, J. Araki, and G. Neubig, “How can we know what language models know?,” 2020
2020
Earlier work this paper cites.
T. Shin, Y. Razeghi, R. L. L. I. au2, E. Wallace, and S. Singh, “Autoprompt: Eliciting knowledge from language models with automatically generated prompts,” 2020
2020
Cited alongside, same era.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al
2020
Cited alongside, same era.
H. Chefer, S. Gur, and L. Wolf, “Transformer interpretability beyond attention visualization,” 2021
2021
Cited alongside, same era.
L. Reynolds and K. McDonell, “Prompt programming for large language models: Beyond the few-shot paradigm,” 2021
2021
Cited alongside, same era.
C. Guo, A. Sablayrolles, H. Jégou, and D. Kiela, “Gradient-based adversarial attacks against text transformers,” 2021
2021
Cited alongside, same era.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang, “Sparks of artificial general intelligence: Early experiments with gpt-4,” 2023
2023
Closest in time.
https://transformer-circuits.pub/2023/monosemantic-features/index.html
T. Bricken, A. Templeton, J. Batson, B. Chen, A. Jermyn, T. Conerly, N. Turner, C. Anil, C. Denison, A. Askell, R. Lasenby, Y. Wu, S. Kravec, N. Schiefer, T. Maxwell, N. Joseph, Z. Hatfield-Dodds, A. Tamkin, K. Nguyen, B. McLean, J. E. Burke, T. Hume, S. Carter, T. Henighan, and C. Olah, “Towards monosemanticity: Decomposing language models with dictionary learning,” Transformer Circuits Thread · 2023
Closest in time.
A. Conmy, A. N. Mavor-Parker, A. Lynch, S. Heimersheim, and A. Garriga-Alonso, “Towards automated circuit discovery for mechanistic interpretability,” 2023
2023
Closest in time.
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “Llama: Open and efficient foundation language models,” 2023
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus, “Emergent abilities of large language models,” 2022
2022
Cited alongside, same era.
OpenAI, Nov 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Shi, X. Han, H. Gonen, A. Holtzman, Y. Tsvetkov, and L. Zettlemoyer, “Toward human readable prompt tuning: Kubrick’s the shining is a good movie, and a good prompt too?,” 2022
2022
Cited alongside, same era.
M. Deng, J. Wang, C.-P. Hsieh, Y. Wang, H. Guo, T. Shu, M. Song, E. P. Xing, and Z. Hu, “Rlprompt: Optimizing discrete text prompts with reinforcement learning,” 2022
2022
Cited alongside, same era.
T. Zhang, X. Wang, D. Zhou, D. Schuurmans, and J. E. Gonzalez, “Tempera: Test-time prompting via reinforcement learning,” 2022
2022
Cited alongside, same era.
Closest in time.
E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, E. Goffinet, D. Heslow, J. Launay, Q. Malartic, B. Noune, B. Pannier, and G. Penedo, “Falcon-40B: an open large language model with state-of-the-art performance,” HuggingFace
2023
Closest in time.
2023
Closest in time.
Y. Wen, N. Jain, J. Kirchenbauer, M. Goldblum, J. Geiping, and T. Goldstein, “Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery,” 2023
2023
Closest in time.
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba, “Large language models are human-level prompt engineers,” 2023
2023
Closest in time.
A. Zou, Z. Wang, J. Z. Kolter, and M. Fredrikson, “Universal and transferable adversarial attacks on aligned language models,” 2023
2023
Closest in time.
Y. Hao, Z. Chi, L. Dong, and F. Wei, “Optimizing prompts for text-to-image generation,” 2023
2023
Closest in time.
D.-K. Kim, S. Sohn, L. Logeswaran, D. Shim, and H. Lee, “Multiprompter: Cooperative prompt optimization with multi-agent reinforcement learning,” 2023
2023
Closest in time.
S. Soatto, P. Tabuada, P. Chaudhari, and T. Y. Liu, “Taming ai bots: Controllability of neural states in large language models,” 2023
2023
Closest in time.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Closest in time.
S. G. Patil, T. Zhang, X. Wang, and J. E. Gonzalez, “Gorilla: Large language model connected with massive apis,” 2023
2023
Closest in time.
K. Sivaramakrishnan, V. Sivaramakrishnan, and M. M. K. Oishi, “Stochastic reachability of discrete-time stochastic systems via probability measures,” 2023
2023
Closest in time.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys
2023
Closest in time.