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Recent studies show that large language models (LLMs) struggle with technical standards in telecommunications.
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A. Aghajanyan, S. Gupta, and L. Zettlemoyer, “Intrinsic dimensionality explains the effectiveness of language model fine-tuning,” in Proc. ACL-IJCNLP , 2021, pp. 7319–7328
2021
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J. Liu, “LlamaIndex,” 2022. [Online]. Available: https://github.com/jerryjliu/llama_index
2022
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E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in Proc. ICLR , 2022
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2023
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2023
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I. Karim, K. S. Mubasshir, M. M. Rahman, and E. Bertino, “SPEC5G: A dataset for 5G cellular network protocol analysis,” in Proc. IJCNLP-AACL , 2023, pp. 20–38
2023
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L. Bariah, H. Zou, Q. Zhao, B. Mouhouche, F. Bader, and M. Debbah, “Understanding telecom language through large language models,” in Proc. IEEE Globecom , 2023, pp. 6542–6547
2023
Cited alongside, same era.
A. Maatouk, N. Piovesan, F. Ayed, A. D. Domenico, and M. Debbah, “Large language models for telecom: Forthcoming impact on the industry,” IEEE Commun. Mag , pp. 1–7, 2024
2024
Cited alongside, same era.
R. Zhang, H. Du, Y. Liu, D. Niyato, J. Kang, S. Sun, X. Shen, and H. V. Poor, “Interactive AI with retrieval-augmented generation for next generation networking,” IEEE Netw , pp. 1–1, 2024
2024
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2024
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2024
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2024
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2024
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2024
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2024
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2024
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M. Javaheripi and S. Bubeck, “Phi-2: The surprising power of small language models,” https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
2024
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2024
Cited alongside, same era.
2024
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2024
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2024
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Chroma, “ChromaDB,” https://www.trychroma.com/
2024
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Inferless, “Ms marco: ms-marco-minilm-l-6-v2,” https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2
2024
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N. Alabbasi and O. Erak, “Specializing large language models for telecom networks,” https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge
2024
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