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English Information

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Authors
# Name
1 Claudio Elízio Calazans Campelo(campelo@computacao.ufcg.edu.br)
2 Helen Cavalcanti(helen.cavalcanti@ccc.ufcg.edu.br)

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Reference
# Reference
1 Bostrom, K. and Durrett, G. (2020). Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720.
2 Cavalcanti, H. and Campelo, C. (2024). Dataset of brazilian federal senate session transcriptions from 2023 with relevant topics and stance detection annotations.
3 dos Santos, M. A. (2024). Modelagem de tópicos na estimativa de pontos ideais baseados em discursos de parlamentares.
4 Jiang, H., Wu, Q., Luo, X., Li, D., Lin, C.-Y., Yang, Y., and Qiu, L. (2023). Longllmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression. arXiv preprint arXiv:2310.06839.
5 Pojoni, M.-L., Dumani, L., and Schenkel, R. (2023). Argument-mining from podcasts using chatgpt. In In procs. of the Workshops at International Conference on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, Scotland, UK, volume 3438, pages 129–144.
6 Reuver, M., Verberne, S., and Fokkens, A. (2024). Investigating the robustness of modelling decisions for few-shot cross-topic stance detection: A preregistered study.
7 Santos, P. D. and Goya, D. H. (2021). Automatic twitter stance detection on politically controversial issues: A study on covid-19’s cpi. In Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional, pages 524–535. SBC.
8 Ilker Gul, Lebret, R., and Aberer, K. (2024). Stance detection on social media with finetuned large language models.