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English Information

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Authors
# Name
1 Annie Amorim(annieamorim@id.uff.br)
2 João Vitor de Moraes(joaovitormoraes@id.uff.br)
3 Débora Pina(dbpina@cos.ufrj.br)
4 Aline Paes(alinepaes@ic.uff.br)
5 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Aftan, S. et al. (2023). A survey on bert and its applications. In 2023 20th Learning and Technology Conference (L&T), pages 161–166. IEEE.
2 Amorim, A. et al. (2025). Exploring language model fusion to improve generalization in portuguese hate speech detection. In 2025 28th FUSION, pages 1–8.
3 Choshen, L. et al. (2022). Fusing finetuned models for better pretraining. CoRR, abs/2204.03044.
4 Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proc. of the 2019 NAACL, pages 4171–4186, Minneapolis. ACL.
5 Freire, J. et al. (2008). Provenance for computational tasks: A survey. Computing in Science & Engineering, 10(3):11–21.
6 Herschel, M. et al. (2017). A survey on provenance: What for? what form? what from? The VLDB Journal, 26(6):881–906.
7 Howard, J. et al. (2018). Universal language model fine-tuning for text classification. In Proc. of the 56th Annual Meeting of the ACL, Melbourne, pages 328–339. ACL.
8 Kerzel, D. et al. (2021). Towards tracking provenance from machine learning notebooks. In International Conference on Knowledge Discovery and Information Retrieval.
9 Matena, M. et al. (2022). Merging models with fisher-weighted averaging.
10 Moreau, L. et al. (2015). The rationale of prov. Journal of Web Semantics, 35:235–257.
11 Padovani, G. et al. (2025). Provenance tracking in large-scale machine learning systems. In Workshop Proceedings of the 54th International Conference on Parallel Processing, ICPP Workshops ’25, page 167–174, New York, NY, USA. Association for Computing Machinery.
12 Pina, D. et al. (2022). Capturing provenance from deep learning applications using keras-prov and colab: a practical approach. Journal of Information and Data Management, 13(5).
13 Pina, D. et al. (2025). Breadcrumbs for your deep learning model: Following provenance traces with dlprov. Software Impacts, 23:100730.
14 Schelter, S. et al. (2017). Automatically tracking metadata and provenance of machine learning experiments.
15 Schlegel, M. and Sattler, K.-U. (2023). Mlflow2prov: Extracting provenance from machine learning experiments. In Proceedings of the Seventh DEEM. ACM.
16 Souza, R. et al. (2019). Provenance data in the machine learning lifecycle in computational science and engineering. In 2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), pages 1–10.
17 Yadav, P. et al. (2023). Ties-merging: Resolving interference when merging models. In Neural Information Processing Systems.