We propose an effective prompting approach that integrates self-evaluation
guidance through stochastic beam search. Our approach explores the reasoning
search space using a well-calibrated automatic criterion. This enables an
efficient search to produce higher-quality final predictions. With the
self-evaluation guided stochastic beam search, we also balance the
quality--diversity trade-off in the generation of reasoning chains. This allows
our approach to adapt well with majority voting and surpass the corresponding
Codex-backboned baselines by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K,
AQUA, and StrategyQA benchmarks, respectively, in few-shot accuracy. Analysis
of our decompositional reasoning finds it pinpoints logic failures and leads to
higher consistency and robustness.

该研究提出了一种有效的提示方法，通过随机波束搜索融合自我评估指导，可以平衡生成链的质量 - 多样性权衡，并在少次学习的情况下，分别在 GSM8K、AQUA 和 StrategyQA 基准测试中比相应的 Codex-backboned 基线高出 6.34％、9.56％和 5.46％的准确度，同时通过细粒度推理又找到并解决了逻辑失误的问题，提高了一致性和鲁棒性。