构建细粒度推理数据集，让模型解释每条事实的推理过程
Atomic-SNLI: Fine-Grained Natural Language Inference through Atomic Fact Decomposition
- 将句子分解为原子事实，实现逐条推理
- 在原子层面准确率提升37%，同时保持句子级性能
- 适合需要可解释AI的场景，如医疗、法律
当前自然语言推理（NLI）系统主要在句子层面运行，决策缺乏解释性。虽然原子级推理通过将假设分解为单个事实提供了新思路，但我们发现现有模型在细粒度推理上表现不佳，传统假设‘所有原子事实都蕴含才蕴含’不成立。分析显示，模型在原子级别上的表现远低于句子级别。为此，我们提出Atomic-SNLI，通过语言学驱动的生成策略，将SNLI数据集分解并补充高质量原子级样本。实验表明，基于Atomic-SNLI微调的模型在原子推理能力上显著提升37%，同时维持强大的句子级性能，实现精准判断与可解释的事实级推理。
原文摘要 · Abstract (English)
Current Natural Language Inference (NLI) systems primarily operate at the sentence level, providing black-box decisions that lack explanatory power. While atomic-level NLI offers a promising alternative by decomposing hypotheses into individual facts, we demonstrate that the conventional assumption that a hypothesis is entailed only when all its atomic facts are entailed fails in practice due to models' poor performance on fine-grained reasoning. Our analysis reveals that existing models perform substantially worse on atomic level inference compared to sentence level tasks. To address this limitation, we introduce Atomic-SNLI, a novel dataset constructed by decomposing SNLI and enriching it with carefully curated atomic level examples through linguistically informed generation strategies. Experimental results demonstrate that models fine-tuned on Atomic-SNLI achieve significant improvements in atomic reasoning capabilities while maintaining strong sentence level performance, enabling both accurate judgements and transparent, explainable results at the fact level.
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