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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
Earlier work this paper cites.
C. Peng, X. Yang, A. Chen, K. E. Smith, N. PourNejatian, A. B. Costa, C. Martin, M. G. Flores, Y. Zhang, T. Magoc et al. , “A study of generative large language model for medical research and healthcare,” arXiv preprint arXiv:2305.13523 , 2023
Original
2023
Earlier work this paper cites.
A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, “Large language models in medicine,” Nature medicine , vol. 29, no. 8, pp. 1930–1940, 2023
2023
Earlier work this paper 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
Original
2023
Earlier work this paper cites.
L. Bariah, H. Zou, Q. Zhao, B. Mouhouche, F. Bader, and M. Debbah, “Understanding Telecom Language Through Large Language Models,” arXiv preprint arXiv:2306.07933 , 2023
Original
2023
Earlier work this paper cites.

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Then

A. Maatouk, F. Ayed, N. Piovesan, A. D. Domenico, M. Debbah, and Z.-Q. Luo, “TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge,” arXiv preprint arXiv:2310.15051 , 2023
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2023
Later among the works it cites.
OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774 , 2023
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2023
Later among the works it cites.
S. Xiao, Z. Liu, P. Zhang, and N. Muennighoff, “C-pack: Packaged resources to advance general chinese embedding,” 2023
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Later among the works it cites.
Y. Fu, H. Peng, L. Ou, A. Sabharwal, and T. Khot, “Specializing smaller language models towards multi-step reasoning,” arXiv preprint arXiv:2301.12726 , 2023
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2023
Later among the works it cites.

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2024

Telecom Language Models: Must They Be Large?

Piovesan, Nicola, De Domenico, Antonio, Ayed, Fadhel

Understand

The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency.

However, the deployment of these sophisticated models is often hampered by their substantial size and computational demands, raising concerns about their viability in resource-constrained environments.
Addressing this challenge, recent advancements have seen the emergence of small language models that surprisingly exhibit performance comparable to their larger counterparts in many tasks, such as coding and common-sense reasoning.
Phi-2, a compact yet powerful model, exemplifies this new wave of efficient small language models.

Built on

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
Earlier work this paper cites.
C. Peng, X. Yang, A. Chen, K. E. Smith, N. PourNejatian, A. B. Costa, C. Martin, M. G. Flores, Y. Zhang, T. Magoc et al. , “A study of generative large language model for medical research and healthcare,” arXiv preprint arXiv:2305.13523 , 2023
Original
2023
Earlier work this paper cites.
A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, “Large language models in medicine,” Nature medicine , vol. 29, no. 8, pp. 1930–1940, 2023
2023
Earlier work this paper 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
Original
2023
Earlier work this paper cites.
L. Bariah, H. Zou, Q. Zhao, B. Mouhouche, F. Bader, and M. Debbah, “Understanding Telecom Language Through Large Language Models,” arXiv preprint arXiv:2306.07933 , 2023
Original
2023
Earlier work this paper cites.

Similar

Then

A. Maatouk, F. Ayed, N. Piovesan, A. D. Domenico, M. Debbah, and Z.-Q. Luo, “TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge,” arXiv preprint arXiv:2310.15051 , 2023
Original
2023
Later among the works it cites.
OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774 , 2023
Original
2023
Later among the works it cites.
S. Xiao, Z. Liu, P. Zhang, and N. Muennighoff, “C-pack: Packaged resources to advance general chinese embedding,” 2023
2023
Later among the works it cites.
Y. Fu, H. Peng, L. Ou, A. Sabharwal, and T. Khot, “Specializing smaller language models towards multi-step reasoning,” arXiv preprint arXiv:2301.12726 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

S. Gunasekar, Y. Zhang, J. Aneja, C. Cesar, T. Mendes, A. D. Giorno, S. Gopi, M. Javaheripi, P. Kauffmann, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, H. Singh Behl, X. Wang, S. Bubeck, R. Eldan, A. T. Kalai, Y. T. Lee, and Y. Li, “Textbooks are all you need,” June 2023

2023

Cited alongside, same era.

Microsoft, “Phi-2: The surprising power of small language models,” Tech. Rep., Dec. 2023, Accessed on 08/02/2024. [Online]. Available: https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/

2024

Closest in time.