Fetching the paper…
Reading the bibliography…
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently demonstrated impressive performance.
F. Colas, S. Mahesh, F. Pomerleau, M. Liu, and R. Siegwart, “3d path planning and execution for search and rescue ground robots,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 722–727
2013
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia conference on computer and communications security , 2017, pp. 506–519
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. A. Kenk, M. Hassaballah, and J.-F. Brethé, “Human-aware robot navigation in logistics warehouses.” in ICINCO (2) , 2019, pp. 371–378
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
H. Chen, A. Suhr, D. Misra, N. Snavely, and Y. Artzi, “Touchdown: Natural language navigation and spatial reasoning in visual street environments,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 538–12 547
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 et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
W. Wang, R. Wang, L. Wang, Z. Wang, and A. Ye, “Towards a robust deep neural network against adversarial texts: A survey,” ieee transactions on knowledge and data engineering , 2021
2021
Earlier work this paper cites.
K. Mahmood, P. H. Nguyen, L. M. Nguyen, T. Nguyen, and M. Van Dijk, “Besting the black-box: barrier zones for adversarial example defense,” IEEE Access , vol. 10, pp. 1451–1474, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
T. Gao, A. Fisch, and D. Chen, “Making pre-trained language models better few-shot learners,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 3816–3830
2021
Earlier work this paper cites.
T. Schick and H. Schütze, “It’s not just size that matters: Small language models are also few-shot learners,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 2339–2352
2021
Earlier work this paper cites.
T. Le Scao and A. M. Rush, “How many data points is a prompt worth?” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 2627–2636
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 4582–4597
2021
Earlier work this paper cites.
A. Eirale, M. Martini, and M. Chiaberge, “Human-centered navigation and person-following with omnidirectional robot for indoor assistance and monitoring,” Robotics , vol. 11, no. 5, p. 108, 2022
2022
Earlier work this paper cites.
M. H. Meng, G. Bai, S. G. Teo, Z. Hou, Y. Xiao, Y. Lin, and J. S. Dong, “Adversarial robustness of deep neural networks: A survey from a formal verification perspective,” IEEE Transactions on Dependable and Secure Computing , 2022
2022
Earlier work this paper cites.
R. Schumann and S. Riezler, “Analyzing generalization of vision and language navigation to unseen outdoor areas,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 7519–7532
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
H. Strobelt, A. Webson, V. Sanh, B. Hoover, J. Beyer, H. Pfister, and A. M. Rush, “Interactive and visual prompt engineering for ad-hoc task adaptation with large language models,” IEEE transactions on visualization and computer graphics , vol. 29, no. 1, pp. 1146–1156, 2022
2022
Cited alongside, same era.
T. Wu, E. Jiang, A. Donsbach, J. Gray, A. Molina, M. Terry, and C. J. Cai, “Promptchainer: Chaining large language model prompts through visual programming,” in CHI Conference on Human Factors in Computing Systems Extended Abstracts , 2022, pp. 1–10
2022
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Cited alongside, same era.
Y. Cui, S. Huang, J. Zhong, Z. Liu, Y. Wang, C. Sun, B. Li, X. Wang, and A. Khajepour, “Drivellm: Charting the path toward full autonomous driving with large language models,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing, “Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,” March 2023. [Online]. Available: https://lmsys.org/blog/2023-03-30-vicuna/
2023
Later among the works it cites.
OpenAI, “Introducing chatgpt,” https://openai.com/blog/chatgpt, 2023, [Online; accessed 2-August-2023]
2023
Later among the works it cites.
——, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
2023
Later among the works it cites.