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We introduce TeleQnA, the first benchmark dataset designed to evaluate the knowledge of Large Language Models (LLMs) in telecommunications.
M. Richardson, “Mctest: A challenge dataset for the open-domain machine comprehension of text,” in EMNLP 2013 , October 2013
2013
Earlier work this paper cites.
R. Zellers et al. , “Swag: A large-scale adversarial dataset for grounded commonsense inference,” in EMNLP 2018 , October 2018
2018
Earlier work this paper cites.
R. Zellers et al. , “HellaSwag: Can a machine really finish your sentence?” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , Jul. 2019, pp. 4791–4800
2019
Earlier work this paper cites.
M. Sap et al. , “Social IQa: Commonsense reasoning about social interactions,” in EMNLP-IJCNLP 2019 , Nov. 2019, pp. 4463–4473
2019
Earlier work this paper cites.
R. Shah et al. , “When FLUE meets FLANG: Benchmarks and large pretrained language model for financial domain,” in EMNLP 2022 , Dec. 2022, pp. 2322–2335
2022
Cited alongside, same era.
K. Singhal et al. , “Large language models encode clinical knowledge,” Nature , vol. 620, no. 7972, pp. 172–180, Aug 2023. [Online]. Available: https://doi.org/10.1038/s41586-023-06291-2
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Altman Solon, “The opportunity in generative AI for Telecom,” White Paper, September 2023. [Online]. Available: https://pages.awscloud.com/GLOBAL-other-DL-generative-ai-for-telecom-whitepaper-2023-learn.html
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774 , March 2023
2023
Closest in time.
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