  While Large language models (LLMs) have the capability to iteratively reflect
on their own outputs, recent studies have observed their struggles with
knowledge-rich problems without access to external resources. In addition to
the inefficiency of LLMs in self-assessment, we also observe that LLMs struggle
to revisit their predictions despite receiving explicit negative feedback.
Therefore, We propose Mirror, a Multiple-perspective self-reflection method for
knowledge-rich reasoning, to avoid getting stuck at a particular reflection
iteration. Mirror enables LLMs to reflect from multiple-perspective clues,
achieved through a heuristic interaction between a Navigator and a Reasoner. It
guides agents toward diverse yet plausibly reliable reasoning trajectory
without access to ground truth by encouraging (1) diversity of directions
generated by Navigator and (2) agreement among strategically induced
perturbations in responses generated by the Reasoner. The experiments on five
reasoning datasets demonstrate that Mirror's superiority over several
contemporary self-reflection approaches. Additionally, the ablation study
studies clearly indicate that our strategies alleviate the aforementioned
challenges.
