A glance at in-context learning

Yongliang WU, Xu YANG

PDF(738 KB)
PDF(738 KB)
Front. Comput. Sci. ›› 2024, Vol. 18 ›› Issue (5) : 185347. DOI: 10.1007/s11704-024-40013-9
Artificial Intelligence
LETTER

A glance at in-context learning

Author information +
History +

Graphical abstract

Cite this article

Download citation ▾
Yongliang WU, Xu YANG. A glance at in-context learning. Front. Comput. Sci., 2024, 18(5): 185347 https://doi.org/10.1007/s11704-024-40013-9

References

[1]
Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8748−8763
[2]
Sun K, Luo X, Luo M Y. A survey of pretrained language models. In: Proceedings of International Conference on Knowledge Science, Engineering and Management. 2022, 442−456
[3]
Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J D, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D. Language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 159
[4]
Hofstadter D R, Sander E. Surfaces and Essences: Analogy as the Fuel and Fire of Thinking. New York: Basic Books, 2013
[5]
Wen M, Lin R, Wang H, Yang Y, Wen Y, Mai L, Wang J, Zhang H, Zhang W. Large sequence models for sequential decision-making: a survey. Frontiers of Computer Science, 2023, 17( 6): 176349
[6]
Xie S M, Raghunathan A, Liang P, Ma T. An explanation of in-context learning as implicit Bayesian inference. In: Proceedings of the 10th International Conference on Learning Representations. 2021
[7]
Yang X, Wu Y, Yang M, Chen H, Geng X. Exploring diverse in-context configurations for image captioning. In: Proceedings of the 37th Conference on Neural Information Processing Systems. 2024
[8]
Wang L, Li L, Dai D, Chen D, Zhou H, Meng F, Zhou J, Sun X. Label words are anchors: An information flow perspective for understanding in-context learning. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 9840−9855
[9]
Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman F L, Almeida D, Altenschmidt J, Altman S, Anadkat S, others. Gpt-4 Technical Report. 2023, arXiv preprint arXiv:2303.08774
[10]
Li L, Peng J, Chen H, Gao C, Yang X. How to configure good in-context sequence for visual question answering. 2023, arXiv preprint arXiv: 2312.01571

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62206048), Natural Science Foundation of Jiangsu Province (BK20220819), Young Elite Scientists Sponsorship Program of Jiangsu Association for Science and Technology (Tj-2022-027), and the Big Data Computing Center of Southeast University.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2024 Higher Education Press
AI Summary AI Mindmap
PDF(738 KB)

Accesses

Citations

Detail

Sections
Recommended

/