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English Information

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Authors
# Name
1 Gabriel Prenassi(prenassigabriel@aluno.ufsj.edu.br)
2 Guilherme Nascimento(guilhermefonseca@dcc.ufmg.br)
3 Davi Jesus(davireisjesus@aluno.ufsj.edu.br)
4 Washington Cunha(washingtoncunha@dcc.ufmg.br)
5 Marcos Gonçalves(mgoncalv@dcc.ufmg.br)
6 Leonardo Rocha(lcrocha@ufsj.edu.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 BehnamGhader, P., Adlakha, V., Mosbach, M., Bahdanau, D., Chapados, N., and Reddy, S. (2024). Llm2vec: Large language models are secretly powerful text encoders.
2 Chandra, M., Ganguly, D., and Ounis, I. (2025). One size doesn’t fit all: Predicting the number of examples for in-context learning. In Advances in Information Retrieval, pages 67–84, Cham.
3 Cunha, W., França, C., Fonseca, G., Rocha, L., and Gonçalves, M. A. (2023). An effective, efficient, and scalable confidence-based instance selection framework for transformer-based text classification. In Proceedings of the 46th ACM SIGIR, SIGIR ’23, page 665–674.
4 Cunha, W., Rocha, L., and Gonçalves, M. A. (2025). A thorough benchmark of automatic text classification: From traditional approaches to large language models.
5 Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding.
6 Edwards, A. and Camacho-Collados, J. (2024). Language models for text classification: Is in-context learning enough? In Proceedings of the 2024 LREC-COLING 2024, pages 10058–10072, Torino, Italia.
7 Fonseca, G., Prenassi, G., Cunha, W., Gonçalves, M., and Rocha, L. (2024). Estratégias de undersampling para redução de viés em classificação de texto baseada em transformers. In Proceedings of the 30th Brazilian Symposium on Multimedia and the Web, pages 144–152, Porto Alegre, RS, Brasil. SBC.
8 Grattafiori, A. et al. (2024). The llama 3 herd of models.
9 Hochberg, Y. (1988). A sharper bonferroni procedure for multiple tests of significance. Biometrika, 75(4):800–802.
10 Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). Lora: Low-rank adaptation of large language models.
11 Kumar, S. and Talukdar, P. (2021). Reordering examples helps during priming-based few-shot learning. In Findings of ACL-IJCNLP 2021, pages 4507–4518, Online. Association for Computational Linguistics.
12 Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W. (2022). What makes good in-context examples for GPT-3? In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 100–114.
13 Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.
14 Lu, Z., Tian, J., Wei, W., Qu, X., Cheng, Y., Xie, W., and Chen, D. (2024). Mitigating boundary ambiguity and inherent bias for text classification in the era of large language models. In Findings of ACL 2024, pages 7841–7864.
15 Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4):309–317.
16 OpenAI et al. (2024). Gpt-4 technical report.
17 Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 EMNLP.
18 Xu, C., Xu, Y., Wang, S., Liu, Y., Zhu, C., and McAuley, J. (2024). Small models are valuable plug-ins for large language models. In Findings of ACL 2024, pages 283–294.