Consistency-Guided Decoding with Proof-Driven Disambiguation for Three-Way Logical Question Answering

Researchers present CGD-PD, a test-time decoding method that improves large language models' performance on three-way logical question answering (True/False/Unknown) by enforcing negation consistency and resolving epistemic uncertainty through targeted entailment probes. The approach achieves up to 16% relative accuracy improvements on the FOLIO benchmark while reducing spurious Unknown predictions.