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English Information

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Authors
# Name
1 Luciana Bencke(luciana.bencke@inf.ufrgs.br)
2 Felipe Paula(fsfpaula@inf.ufrgs.br)
3 Bruno dos Santos(bruno.tsantos@inf.ufrgs.br)
4 Viviane Pereira Moreira (viviane@inf.ufrgs.br)

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Reference
# Reference
1 Almeida, T. S., Abonizio, H., and Nogueira, R. (2024). Sabiá-2: A New Generation of Portuguese Large Language Models. arXiv preprint arXiv:2403.09887
2 Blanco, R., Halpin, H., Herzig, D. M., Mika, P., Pound, J., Thompson, H. S., and Tran Duc, T. (2011). Repeatable and reliable search system evaluation using crowdsourcing. In ACM SIGIR conference on Research and development in Information Retrieval, pages 923–932.
3 Bueno, M., de Oliveira, E. S., Nogueira, R., Lotufo, R. A., and Pereira, J. A. (2024). Quati: A brazilian portuguese information retrieval dataset from native speakers. arXiv preprint arXiv:2404.06976.
4 Cleverdon, C. W. (1960). The aslib cranfield research project on the comparative efficiency of indexing systems. In Aslib Proceedings, volume 12, pages 421–431.
5 de Jesus, G. and Nunes, S. (2024). Exploring large language models for relevance judgments in tetun. arXiv preprint arXiv:2406.07299.
6 Faggioli, G., Dietz, L., Clarke, C. L., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., et al. (2023). Perspectives on large language models for relevance judgment. In ACM SIGIR International Conference on Theory of Information Retrieval, pages 39–50.
7 Lima de Oliveira, L., Romeu, R. K., and Moreira, V. P. (2021). REGIS: A Test Collection for Geoscientific Documents in Portuguese. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 2363–2368.
8 Piau, M., Lotufo, R., and Nogueira, R. (2024). ptt5-v2: A closer look at continued pretraining of T5 models for the portuguese language. arXiv preprint arXiv:2406.10806.
9 Rahmani, H. A., Craswell, N., Yilmaz, E., Mitra, B., and Campos, D. (2024). Synthetic test collections for retrieval evaluation. arXiv preprint arXiv:2405.07767.
10 Resnick, A. and Savage, T. R. (1964). The consistency of human judgments of relevance. American Documentation, 15(2):93–95. Santos, D. and Rocha, P. (2004). The key to the first CLEF with Portuguese: Topics, questions and answers in CHAVE. In Workshop of the Cross-Language Evaluation Forum for European Languages, pages 821–832. Springer.
11 Soviero, B., Kuhn, D., Salle, A., and Moreira, V. P. (2024). ChatGPT goes shopping: LLMs can predict relevance in ecommerce search. In European Conference on Information Retrieval, pages 3–11.
12 Spärck Jones, K. and van Rijsbergen, C. J. (1975). Report on the need for and provision of an "ideal" information retrieval test collection. Computer Laboratory, University of Cambridge.
13 Spärck Jones, K.,Walker, S., and Robertson, S. E. (2000). A probabilistic model of information retrieval: development and comparative experiments. Information processing & management, 36(6):809–840.
14 Theodosiou, Z., Georgiou, O., and Tsapatsoulis, N. (2011). Evaluating annotators consistency with the aid of an innovative database schema. In International Workshop on Semantic Media Adaptation and Personalization, pages 74–78.
15 Thomas, P., Spielman, S., Craswell, N., and Mitra, B. (2023). Large language models can accurately predict searcher preferences. arXiv preprint arXiv:2309.10621.
16 Voorhees, E. M. (2000). Variations in relevance judgments and the measurement of retrieval effectiveness. Information Processing & Management, 36(5):697–716.
17 Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., andWei, F. (2024). Multilingual e5 text embeddings: A technical report. arXiv preprint arXiv:2402.05672.
18 Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al. (2024). Judging LLM-as-a-judge with MT-bench and chatbot arena. Advances in Neural Information Processing Systems, 36.
19 Zhu, E., Sheng, Q., Yang, H., Liu, Y., Cai, T., and Li, J. (2023). A unified framework of medical information annotation and extraction for chinese clinical text. Artificial Intelligence in Medicine, 142:102573.