Can We Do Interpretable NLI with Graphs Based on Atomic Propositions?

2026-09-15

AI Summary

本文探索使用基于原子命题的图表示来进行可解释的自然语言推理，通过将句子转换为图并输入到语言模型中，尽管在某些数据集上略低于文本模型，但实现了较高准确性。

Abstract

While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures. This paper explores whether NLI can be performed using only interpretable, graph-based representations of evidence. We introduce a fully graph-based pipeline where the classifier never directly processes the input text. Instead, sentences are decomposed into atomic propositions, converted into ConceptNet triples via constrained decoding, and represented as three graphs per pair: premise, hypothesis, and a retrieved ConceptNet subgraph. These graphs are then fed into a fine-tuned 0.8-billion-parameter language model. On the SNLI dataset, our pipeline achieves 89.7% accuracy, just 1.9 points below an identically trained text-based model. On ANLI, it matches the published performance of RoBERTa-large on rounds R2 and R3 (50% accuracy) but trails by 16 points on R1, resulting in an overall gap of 9 to 14 points compared to its text counterpart. We term this gap the price of interpretability and demonstrate that it stems from representational limitations rather than data constraints. Ablation studies further reveal that graphs and text are complementary: combining both modalities achieves 92.1% accuracy on SNLI.

Problem

• Natural Language Inference
• Interpretable
• Graph-based Representation
• Auditable Structures

Innovation

• Graph-based NLI
• Atomic Propositions
• Interpretable Representations
• ConceptNet Triples