Strong Matchers, Weak Architecture: Towards Confidence Guided Global Consistency in LLM-based Record Linkage - Pires, Luma C. R.; A. Junior, Jorge R. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Record Linkage produces transitive inconsistencies that existing LLM-based methods address locally or by construction, but none optimizes at partition level. We investigate whether LLM confidence scores carry sufficient signal to drive global partition optimization in clean-clean multi-source settings. On MusicBrainz, scores discriminate true from false matches among predicted matches (mean 0.936 vs. 0.729). A global ILP exploiting scores as edge weights resolves all 42 inconsistent components by removing 52 edges (98.1% true false positives) fewer than greedy alternatives and with an optimality guarantee. These results motivate LLM confidence scores as input to partition-level optimization, with efficient approximations as future work.