Can Small Language Models With Retrieval-Augmented Generation Replace Large Language Models When Learning Computer Science?-Reference-Cited by-同舟云学术

Can Small Language Models With Retrieval-Augmented Generation Replace Large Language Models When Learning Computer Science?

Published:2024-07-03 Issue: Volume:33 Page:388-393
ISSN:
Container-title:Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1
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Author:

Liu Suqing¹^ORCID,Yu Zezhu¹^ORCID,Huang Feiran¹^ORCID,Bulbulia Yousef¹^ORCID,Bergen Andreas¹^ORCID,Liut Michael²^ORCID

Affiliation:

1. University of Toronto Mississauga, Mississauga, Ontario, Canada

2. University of Toronto Mississauga, Mississauga, Canada

Funder

University of Toronto

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3649217.3653554

Reference47 articles.

1. Erfan Al-Hossami, Razvan Bunescu, Ryan Teehan, Laurel Powell, and Khyati Mahajan et al. 2023. Socratic questioning of novice debuggers: A benchmark dataset and preliminary evaluations. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). 709--726.

2. Gemini Team Google: Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, et al. 2023. Gemini: a family of highly capable multimodal models. arxiv: 2312.11805 [cs.CL]

3. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. arXiv preprint arXiv:2310.11511 (2023).

4. The Social Impact of Generative AI: An Analysis on ChatGPT

5. Ron Banner, Yury Nahshan, Elad Hoffer, and Daniel Soudry. 2019. Post training 4-bit quantization of convolutional networks for rapid-deployment. Advances in Neural Information Processing Systems (2019).