Large Language Models (LLMs) have shown human-like reasoning abilities but
still face challenges in solving complex logical problems. Existing
unidirectional chaining methods, such as forward chaining and backward
chaining, suffer from issues like low prediction accuracy and efficiency. To
address these, we propose a bidirectional chaining method, Bi-Chainer, which
dynamically switches to depth-first reasoning in the opposite reasoning
direction when it encounters multiple branching options within the current
direction. Thus, the intermediate reasoning results can be utilized as guidance
to facilitate the reasoning process. We show that Bi-Chainer achieves sizable
accuracy boots over unidirectional chaining frameworks on four challenging
logical reasoning datasets. Moreover, Bi-Chainer enhances the accuracy of
intermediate proof steps and reduces the average number of inference calls,
resulting in more efficient and accurate reasoning.

提出了一种双向链式方法 Bi-Chainer，通过动态切换至深度优先推理来解决单向链式方法在复杂逻辑问题中的低预测准确性和效率问题，并展示了在四个具有挑战性的逻辑推理数据集上，Bi-Chainer 相较于单向链式框架实现了可观的准确度提升。此外，Bi-Chainer 提升了中间证明步骤的准确度，同时减少了推理调用的平均次数，从而实现更高效准确的推理。