KERLQA: Knowledge-Enhanced Reinforcement Learning for Question Answering in Low-resource Languages

AbstractQuestion answering in low-resource languages faces critical challenges when models encounter questions beyond their knowledge boundaries, often producing confident but incorrect answers. We propose Knowledge-Enhanced Reinforcement Learning for Question Answering (KERLQA), a novel approach that combines knowledge graph integration with reinforcement learning to enable principled abstention decisions. Unlike existing refusal-tuned methods that make binary decisions based solely on internal confidence, KERLQA implements a three-way decision process: answer with internal knowledge, answer with external knowledge assistance, or abstain. Using a composite reward function that jointly optimizes for correctness, appropriate abstention, and efficient knowledge utilization, we train policies via PPO and DPO with dynamic calibration for low-resource settings. Experiments on CommonsenseQA and OpenBookQA across English and four South African languages show KERLQA achieves improved F1 scores, with up to 6.2 point improvements in low-resource languages. Our analysis reveals that KERLQA reduces false positive abstention rates by 30% while expanding the boundary of answerable questions through external knowledge integration.