Document-level Relation Extraction Via Self-Correcting Prompt Optimization and Preference Alignment

Document-level relation extraction (DocRE) finds relations across a whole document. It often needs evidence from several sentences. It also needs to link repeated entity mentions and follow multi-hop clues. Many large language model (LLM) based DocRE methods use fixed relation descriptions. They make little use of confident errors or samples with missing labels. We propose SPO-PA, which combines self-correcting prompt optimization (SPO) and preference alignment (PA). SPO compares LLM predictions with reference annotations. It then revises relation descriptions, clarifies relation boundaries, and adds role constraints. PA uses false negative (FN) conflicts as the main supervision signal. It also tests false positive (FP) conflicts under similar constraints. PA applies source-specific constraints to these signals. The constraints help smaller DocRE models separate correct relations from wrong candidates. On Re-DocRED, SPO improves F1 to 26.23 PA_FN gives the most stable gains on ATLOP, KD-DocRE, DREEAM, and DAATF. The largest gain is 2.89