With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models are faithful, i.e. that they reflect the inner workings of the model. Atomic inference overcomes this issue, providing interpretable and faithful model decisions. This approach involves making predictions for different components (or atoms) of an instance, before using interpretable and deterministic rules to derive the overall prediction based on the individual atom-level predictions. We investigate the effectiveness of using LLM-generated facts as atoms, decomposing Natural Language Inference premises into lists of facts. While directly using generated facts in atomic inference systems can result in worse performance, with 1) a multi-stage fact generation process, and 2) a training regime that incorporates the facts, our fact-based method outperforms other approaches.

翻译：随着近期技术的进步，神经模型在各种自然语言任务上已达到人类水平的表现。然而，这些模型提供的任何解释都无法保证其忠实性，即它们是否真实反映了模型的内在运作机制。原子推理通过为实例的不同组成部分（或称原子）分别进行预测，再基于可解释的确定性规则，根据各原子层面的预测推导出整体预测，从而克服了这一问题，提供了可解释且忠实的模型决策。本研究探讨了使用大语言模型生成的事实作为原子的有效性，将自然语言推理的前提分解为事实列表。虽然直接在原子推理系统中使用生成的事实可能导致性能下降，但通过采用1）多阶段事实生成流程，以及2）融入事实的训练机制，我们基于事实的方法在性能上超越了其他现有方法。