Large language models (LLMs) often generate convincing, fluent explanations.
However, different from humans, they often generate inconsistent explanations
on different inputs. For example, an LLM may generate the explanation "all
birds can fly" when answering the question "Can sparrows fly?" but meanwhile
answer "no" to the related question "Can penguins fly?". Explanations should be
consistent across related examples so that they allow a human to simulate the
LLM's decision process on multiple examples. We propose explanation-consistency
finetuning (EC-finetuning), a method that adapts LLMs to generate more
consistent natural-language explanations on related examples. EC-finetuning
involves finetuning LLMs on synthetic data that is carefully constructed to
contain consistent explanations. Across a variety of question-answering
datasets in various domains, EC-finetuning yields a 10.0% relative explanation
consistency improvement on four finetuning datasets, and generalizes to seven
out-of-distribution datasets not seen during finetuning (+4.5% relative). Code
is available at this https URL .

大语言模型经常生成有说服力和流畅的解释，但与人类不同，它们在不同输入上生成的解释通常不一致。本文提出了一种解释一致性微调的方法，通过对经过精心构造的合理解释的合成数据进行微调，使得大语言模型在相关示例上生成更一致的自然语言解释。这种方法在各种领域的问答数据集上获得了 10.0% 的相对解释一致性改善，并且在 7 个未在微调过程中使用的外部数据集上实现了 4.5% 的相对泛化改进。