Built on

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized BERT pretraining approach,” CoRR , vol. abs/1907.11692, 2019. [Online]. Available: http://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Austin, Texas: Association for Computational Linguistics, Nov. 2016, pp. 2383–2392. [Online]. Available: https://www.aclweb.org/anthology/D16-1264
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” CoRR , vol. abs/1706.03762, 2017. [Online]. Available: http://arxiv.org/abs/1706.03762
Original
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” CoRR , vol. abs/1810.04805, 2018. [Online]. Available: http://arxiv.org/abs/1810.04805
Original
2018
Earlier work this paper cites.
P. Rajpurkar, R. Jia, and P. Liang, “Know what you don’t know: Unanswerable questions for SQuAD,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, 2018, pp. 784–789. [Online]. Available: https://www.aclweb.org/anthology/P18-2124
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
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.
P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” CoRR , vol. abs/2005.11401, 2020
Original
2020
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” in International Conference on Learning Representations , 2020
2020
Earlier work this paper cites.
H. Holm, “Bidirectional encoder representations from transformers (bert) for question answering in the telecom domain.: Adapting a bert-like language model to the telecom domain using the electra pre-training approach,” 2021
2021
Earlier work this paper cites.

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M. Gunnarsson, “Multi-hop neural question answering in the telecom domain,” 2021
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T. Lin, Y. Wang, X. Liu, and X. Qiu, “A survey of transformers,” AI Open , 2022
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G. Gerganov, “Ggml library: Large language models for everyone,” https://github.com/rustformers/llm/tree/main/crates/ggml , 2022
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W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023
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Cited alongside, same era.

Then

Y. Chen, R. Li, Z. Zhao, C. Peng, J. Wu, E. Hossain, and H. Zhang, “Netgpt: A native-ai network architecture beyond provisioning personalized generative services,” arXiv preprint arXiv:2307.06148 , 2023
Original
2023
Later among the works it cites.
Y. Huang, M. Xu, X. Zhang, D. Niyato, Z. Xiong, S. Wang, and T. Huang, “Ai-generated network design: A diffusion model-based learning approach,” IEEE Network , pp. 1–1, 2023
2023
Later among the works it cites.
C. Wang, M. Scazzariello, A. Farshin, D. Kostic, and M. Chiesa, “Making network configuration human friendly,” arXiv preprint arXiv:2309.06342 , 2023
Original
2023
Later among the works it 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
Later among the works it cites.

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2024

Using Large Language Models to Understand Telecom Standards

Karapantelakis, Athanasios, Thakur, Mukesh, Nikou, Alexandros et al.

Understand

The Third Generation Partnership Project (3GPP) has successfully introduced standards for global mobility.

However, the volume and complexity of these standards has increased over time, thus complicating access to relevant information for vendors and service providers.
Use of Generative Artificial Intelligence (AI) and in particular Large Language Models (LLMs), may provide faster access to relevant information.
In this paper, we evaluate the capability of state-of-art LLMs to be used as Question Answering (QA) assistants for 3GPP document reference.

Built on

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized BERT pretraining approach,” CoRR , vol. abs/1907.11692, 2019. [Online]. Available: http://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Austin, Texas: Association for Computational Linguistics, Nov. 2016, pp. 2383–2392. [Online]. Available: https://www.aclweb.org/anthology/D16-1264
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” CoRR , vol. abs/1706.03762, 2017. [Online]. Available: http://arxiv.org/abs/1706.03762
Original
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” CoRR , vol. abs/1810.04805, 2018. [Online]. Available: http://arxiv.org/abs/1810.04805
Original
2018
Earlier work this paper cites.
P. Rajpurkar, R. Jia, and P. Liang, “Know what you don’t know: Unanswerable questions for SQuAD,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, 2018, pp. 784–789. [Online]. Available: https://www.aclweb.org/anthology/P18-2124
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
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.
P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” CoRR , vol. abs/2005.11401, 2020
Original
2020
Earlier work this paper cites.
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” in International Conference on Learning Representations , 2020
2020
Earlier work this paper cites.
H. Holm, “Bidirectional encoder representations from transformers (bert) for question answering in the telecom domain.: Adapting a bert-like language model to the telecom domain using the electra pre-training approach,” 2021
2021
Earlier work this paper cites.

Similar

M. Gunnarsson, “Multi-hop neural question answering in the telecom domain,” 2021
2021
Cited alongside, same era.
T. Lin, Y. Wang, X. Liu, and X. Qiu, “A survey of transformers,” AI Open , 2022
2022
Cited alongside, same era.
G. Gerganov, “Ggml library: Large language models for everyone,” https://github.com/rustformers/llm/tree/main/crates/ggml , 2022
2022
Cited alongside, same era.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023
Original
2023
Cited alongside, same era.
OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
Original
2023
Cited alongside, same era.

Then

Y. Chen, R. Li, Z. Zhao, C. Peng, J. Wu, E. Hossain, and H. Zhang, “Netgpt: A native-ai network architecture beyond provisioning personalized generative services,” arXiv preprint arXiv:2307.06148 , 2023
Original
2023
Later among the works it cites.
Y. Huang, M. Xu, X. Zhang, D. Niyato, Z. Xiong, S. Wang, and T. Huang, “Ai-generated network design: A diffusion model-based learning approach,” IEEE Network , pp. 1–1, 2023
2023
Later among the works it cites.
C. Wang, M. Scazzariello, A. Farshin, D. Kostic, and M. Chiesa, “Making network configuration human friendly,” arXiv preprint arXiv:2309.06342 , 2023
Original
2023
Later among the works it 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
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…

H. T. et all, “Llama 2: Open foundation and fine-tuned chat models,” 2023

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Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, L. Li, and Z. Sui, “A survey on in-context learning,” arXiv preprint arXiv:2301.00234 , 2023

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J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt, “A prompt pattern catalog to enhance prompt engineering with chatgpt,” 2023

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Y. Wang, W. Zhong, L. Li, F. Mi, X. Zeng, W. Huang, L. Shang, X. Jiang, and Q. Liu, “Aligning large language models with human: A survey,” arXiv preprint arXiv:2307.12966 , 2023

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A. Karapantelakis, P. Alizadeh, A. Alabassi, K. Dey, and A. Nikou, “Generative ai in mobile networks: a survey,” Annals Telecommunications, Link: https://link.springer.com/article/10.1007/s12243-023-00980-9 , 2023

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Cited alongside, same era.

L. Bariah, Q. Zhao, H. Zou, Y. Tian, F. Bader, and M. Debbah, “Large language models for telecom: The next big thing?” arXiv preprint arXiv:2306.10249 , 2023

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2023

Cited alongside, same era.

Y. Huang, H. Du, X. Zhang, D. Niyato, J. Kang, Z. Xiong, S. Wang, and T. Huang, “Large language models for networking: Applications, enabling techniques, and challenges,” arXiv preprint arXiv:2311.17474 , 2023

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Cited alongside, same era.

S. Soman et al. , “Observations on llms for telecom domain: Capabilities and limitations,” arXiv preprint arXiv:2305.13102 , 2023

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2023