On Provenance in Model Fusion - Amorim, Annie; Moraes, João Vitor; Pina, Débora; Paes, Aline; de Oliveira, Daniel (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
As the number of domain-specific language models grows at a fast pace, managing multiple specialized models becomes a challenge. Model fusion is an alternative that combines models to improve robustness. However, fusion workflows involve steps such as model selection, merging strategies, evaluation, and repeated iterations. Selecting the best combination of source models requires understanding the full data derivation path. In this paper, we present FusionProv, an approach compliant with the W3C PROV recommendation that captures provenance from fusion workflows to support traceability and analytics. We evaluate FusionProv in a hate-speech detection application that leverages fused language models.