Large language models have manifested remarkable capabilities by leveraging
chain-of-thought (CoT) reasoning techniques to solve intricate questions
through step-by-step reasoning chains. Despite its success, the efficacy of
such reasoning is inherently contingent upon the quality of CoT. However,
flawless CoT reasoning cannot be guaranteed due to the presence of
indecomposable questions and the potential for erroneous reasoning chains,
particularly in the case of small-scale language models. To tackle this
challenge, we propose a novel approach called the selective filtering reasoner
(SelF-Reasoner) that assesses the entailment relationship between the question
and the candidate reasoning chain. Then, we proceed with CoT reasoning when the
reasoning chain demonstrates confidence; otherwise, we opt to predict the
answer directly. SelF-Reasoner improves the fine-tuned T5 baseline consistently
over the ScienceQA, ECQA, and LastLetter tasks. Code is available at
\texttt{this https URL}.

通过选择性过滤推理程序（SelF-Reasoner）提高了大型语言模型的链式思考推理能力，进而改进了基于 T5 模型的科学问题、Bluetooth 问题和最后一个字母问题的解答质量。