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In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question answering.
1907
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Eds., vol. 25. Curran Associates, Inc., 2012. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
2012
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in Neural Information Processing Systems , C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, Eds., vol. 26. Curran Associates, Inc., 2013. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
A. Radford and K. Narasimhan, “Improving language understanding by generative pre-training,” 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:49313245
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , J. Burstein, C. Doran, and T. Solorio, Eds. Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
I. Beltagy, K. Lo, and A. Cohan, “SciBERT: A pretrained language model for scientific text,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 3615–3620. [Online]. Available: https://aclanthology.org/D19-1371
2019
Earlier work this paper cites.
K. Huang, J. Altosaar, and R. Ranganath, “Clinicalbert: Modeling clinical notes and predicting hospital readmission,” 2019
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:160025533
2019
Earlier work this paper cites.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A lite BERT for self-supervised learning of language representations,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=H1eA7AEtvS
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, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–1901. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
Earlier work this paper cites.
Z. Liu, D. Huang, K. Huang, Z. Li, and J. Zhao, “Finbert: a pre-trained financial language representation model for financial text mining,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence , ser. IJCAI’20, 2021
2021
Earlier work this paper cites.
J. Tang, W. Zhang, H. Liu, M. Yang, B. Jiang, G. Hu, and X. Bai, “Few could be better than all: Feature sampling and grouping for scene text detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 4563–4572
2022
Earlier work this paper cites.
J. Tang, S. Qiao, B. Cui, Y. Ma, S. Zhang, and D. Kanoulas, “You can even annotate text with voice: Transcription-only-supervised text spotting,” in Proceedings of the 30th ACM International Conference on Multimedia , ser. MM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 4154–4163. [Online]. Available: https://doi.org/10.1145/3503161.3547787
2022
Earlier work this paper cites.
J. Tang, W. Qian, L. Song, X. Dong, L. Li, and X. Bai, “Optimal boxes: Boosting end-to-end scene text recognition by adjusting annotated bounding boxes via reinforcement learning,” in Computer Vision – ECCV 2022 , S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds. Cham: Springer Nature Switzerland, 2022, pp. 233–248
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
3GPP, “Reliable and Low Latency Communication,” 2023, Accessed: 2024-12-23. [Online]. Available: https://www.3gpp.org/technologies/urlcc-2022
2022
Cited alongside, same era.
Y. Liu, J. Zhang, D. Peng, M. Huang, X. Wang, J. Tang, C. Huang, D. Lin, C. Shen, X. Bai, and L. Jin, “Spts v2: Single-point scene text spotting,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 12, pp. 15 665–15 679, 2023
2023
Cited alongside, same era.
J. Tang, W. Du, B. Wang, W. Zhou, S. Mei, T. Xue, X. Xu, and H. Zhang, “Character recognition competition for street view shop signs,” National Science Review , vol. 10, no. 6, p. nwad141, 05 2023. [Online]. Available: https://doi.org/10.1093/nsr/nwad141
2023
Cited alongside, same era.
Z. Zhao, J. Tang, C. Lin, B. Wu, C. Huang, H. Liu, X. Tan, Z. Zhang, and Y. Xie, “Multi-modal in-context learning makes an ego-evolving scene text recognizer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 15 567–15 576
2024
Later among the works it cites.
2024
Later among the works it cites.
——, “The llama 3 herd of models,” 2024. [Online]. Available: https://arxiv.org/abs/2407.21783
2024
Later among the works it cites.
J. Zhang, Z. Mai, Z. Xu, and Z. Xiao, “Is llama 3 good at identifying emotion? a comprehensive study,” in Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI) , ser. MLMI ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 128–132. [Online]. Available: https://doi.org/10.1145/3696271.3696292
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Y. Tao, “SQBA: sequential query-based blackbox attack,” in Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023) , H. Zaidi, Y. S. Shmaliy, H. Meng, H. Kolivand, Y. Sun, J. Luo, and M. Alazab, Eds., vol. 12803, International Society for Optics and Photonics. SPIE, 2023, p. 128032Q. [Online]. Available: https://doi.org/10.1117/12.3009240
2023
Cited alongside, same era.
——, “Meta learning enabled adversarial defense,” in 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) , 2023, pp. 1326–1330
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Xiao, Z. Mai, Z. Xu, Y. Cui, and J. Li, “Corporate event predictions using large language models,” in 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI) . IEEE, 2023, pp. 193–197
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Mai, J. Zhang, Z. Xu, and Z. Xiao, “Financial sentiment analysis meets llama 3: A comprehensive analysis,” in Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI) , ser. MLMI ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 171–175. [Online]. Available: https://doi.org/10.1145/3696271.3696299
2024
Cited alongside, same era.
2024
Later among the works it cites.
Z. Xiao, Y. Huang, and E. Blanco, “Analyzing large language models’ capability in location prediction,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, and N. Xue, Eds. Torino, Italia: ELRA and ICCL, May 2024, pp. 951–958. [Online]. Available: https://aclanthology.org/2024.lrec-main.85
2024
Later among the works it cites.
Z. Xiao, Z. Mai, Y. Cui, Z. Xu, and J. Li, “Short interest trend prediction with large language models,” in Proceedings of the 2024 International Conference on Innovation in Artificial Intelligence , ser. ICIAI ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 1. [Online]. Available: https://doi.org/10.1145/3655497.3655500
2024
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Proceedings of the 36th International Conference on Neural Information Processing Systems , ser. NIPS ’22. Red Hook, NY, USA: Curran Associates Inc., 2024
2024
Later among the works it cites.
X. Zhao, M. Li, W. Lu, C. Weber, J. H. Lee, K. Chu, and S. Wermter, “Enhancing zero-shot chain-of-thought reasoning in large language models through logic,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, and N. Xue, Eds. Torino, Italia: ELRA and ICCL, May 2024, pp. 6144–6166. [Online]. Available: https://aclanthology.org/2024.lrec-main.543
2024
Later among the works it cites.
2024
Later among the works it cites.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: deliberate problem solving with large language models,” in Proceedings of the 37th International Conference on Neural Information Processing Systems , ser. NIPS ’23. Red Hook, NY, USA: Curran Associates Inc., 2024
2024
Later among the works it cites.
Y. Yao, Z. Li, and H. Zhao, “GoT: Effective graph-of-thought reasoning in language models,” in Findings of the Association for Computational Linguistics: NAACL 2024 , K. Duh, H. Gomez, and S. Bethard, Eds. Mexico City, Mexico: Association for Computational Linguistics, Jun. 2024, pp. 2901–2921. [Online]. Available: https://aclanthology.org/2024.findings-naacl.183
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Wu, Z. Xiao, J. Zhang, Z. Mai, and Z. Xu, “Can llama 3 understand monetary policy?” in 2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE) , 2024, pp. 145–149
2024
Later among the works it cites.
M. Wan, T. Safavi, S. K. Jauhar, Y. Kim, S. Counts, J. Neville, S. Suri, C. Shah, R. W. White, L. Yang, R. Andersen, G. Buscher, D. Joshi, and N. Rangan, “Tnt-llm: Text mining at scale with large language models,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 5836–5847. [Online]. Available: https://doi.org/10.1145/3637528.3671647
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Mai, J. Zhang, Z. Xu, and Z. Xiao, “Is llama 3 good at sarcasm detection? a comprehensive study,” in Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI) , ser. MLMI ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 141–145. [Online]. Available: https://doi.org/10.1145/3696271.3696294
2024
Later among the works it cites.
J. Zhang, K. Lu, Y. Wan, J. Xie, and S. Fu, “Empowering uav-based airborne computing platform with sdr: Building an lte base station for enhanced aerial connectivity,” IEEE Transactions on Vehicular Technology , 2024
2024
Later among the works it cites.