While large language models (LLMs) are proficient at question-answering (QA),
the dependencies between their answers and other "beliefs" they may have about
the world are typically unstated, and may even be in conflict. Our goal is to
uncover such dependencies and reduce inconsistencies among them, so that
answers are supported by faithful, system-believed chains of reasoning drawn
from a consistent network of beliefs. Our approach, which we call REFLEX, is to
add a "rational", self-reflecting layer on top of the LLM. First, given a
question, we construct a belief graph using a backward-chaining process to
materialize relevant model "beliefs" (including beliefs about answer
candidates) and the inferential relationships between them. Second, we identify
and minimize contradictions in that graph using a formal constraint reasoner.
We find that REFLEX significantly improves consistency (by 8%-11% absolute)
without harming overall answer accuracy, resulting in answers supported by
faithful chains of reasoning drawn from a more consistent belief system. This
suggests a new style of system architecture, in which an LLM extended with a
rational layer of self-reflection can repair latent inconsistencies within the
LLM alone.

在大型语言模型中添加一个反思层次，通过构建信念图和使用形式化的约束推理器，从而减少其答案与其他 “信念” 的依赖关系和不一致性，REFLEX 方法可以改善一定程度的一致性，同时不降低模型的答案准确性。