Critic Rule Induction: Improving Temporal Knowledge Graph Forecasting with Generator-Critic Language Models

摘要

Temporal knowledge graph (TKG) forecasting aims to infer future facts from historical observations in time-evolving graphs. Traditional rule-based methods often rely on statistical co-occurrences and extensive path enumeration, suffering from rule sparsity and search-space explosion, while recent LLM-based rule reasoning can produce linguistically plausible rules that are weakly constrained by graph evidence and thus may reflect spurious correlations or violate temporal constraints.To address these challenges, we propose Critic-Guided Rule Induction (CRI), which treats temporal rules as rule hypotheses to be examined and adopts a decoupled Generation-Discrimination pipeline to induce rules that are both high-coverage and high-precision. CRI first mines seed rules and path evidence from the historical graph and uses an LLM-based generator to abstract and generalize them into broader raw rule hypotheses. It then introduces a Fact-Grounded Rule Evaluator to perform fact-grounded discrimination of rule hypotheses from complementary perspectives together with necessary temporal and statistical constraints. Finally, CRI performs symbolic reasoning over the refined rule set to produce forecasts with traceable reasoning evidence. Experiments on three benchmarks show that CRI outperforms strong baselines, achieving state-of-the-art performance on TKG forecasting.