Binary neural networks (BNNs) have emerged as a promising solution for efficient low-level vision information processing, particularly in resource-constrained environments. However, applying BNNs to low-light image enhancement (LLIE) remains almost unexplored. A significant challenge in current BNNs is their limited ability to effectively address various degradation factors present in low-light images. To overcome this, we propose a framework named Binarized Low-Light Image Enhancement (BiLLIE), enabling smooth binarization of LLIE models. By introducing Adaptive Information Flow mechanism, the degradation factors in low-light images can be effectively controlled. In addition, we employ a two-step training strategy, aiming to gradually binarize LLIE models. We evaluated BiLLIE against seven BNN models on three binarized LLIE models. Experimental results demonstrate that BiLLIE achieves superior enhancement performance while significantly reducing computational and storage costs.