TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361 , 2020

Original

2020

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.

K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang, “REALM: Retrieval-Augmented Language Model Pre-Training,” in International conference on machine learning . PMLR, 2020, pp. 3929–3938

2020

Earlier work this paper cites.

P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel et al. , “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 9459–9474, 2020

2020

Earlier work this paper cites.

V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” arXiv preprint arXiv:2004.04906 , 2020

Original

2020

Cited alongside, same era.

3rd Generation Partnership Project (3GPP), “IP Multimedia Subsystem (IMS) Application Level Gateway (IMS-ALG) - IMS Access Gateway (IMS-AGW) interface,” 3rd Generation Partnership Project (3GPP), Technical Specification 23.334, 2021

2021

Cited alongside, same era.

J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler et al. , “Emergent abilities of large language models,” arXiv preprint arXiv:2206.07682 , 2022

Original

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

R. Taylor, M. Kardas, G. Cucurull, T. Scialom, A. Hartshorn, E. Saravia, A. Poulton, V. Kerkez, and R. Stojnic, “Galactica: A large language model for science,” arXiv preprint arXiv:2211.09085 , 2022

Original

2022

Later among the works it cites.

A. Lewkowycz, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil, I. Schlag, T. Gutman-Solo et al. , “Solving quantitative reasoning problems with language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 3843–3857, 2022

2022

Later among the works it cites.

K. Jeblick, B. Schachtner, J. Dexl, A. Mittermeier, A. T. Stüber, J. Topalis, T. Weber, P. Wesp, B. O. Sabel, J. Ricke et al. , “Chatgpt makes medicine easy to swallow: an exploratory case study on simplified radiology reports,” European radiology , pp. 1–9, 2023

2023

Later among the works it cites.

S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann, “Bloomberggpt: A large language model for finance,” arXiv preprint arXiv:2303.17564 , 2023