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

arXiv:2609.16814 · cs.AI, cs.IR · Submitted 2026-09-15 · Read on arXiv

cs.AI, cs.IR

Submitted: 2026-09-15

Updated: 2026-09-17

Journal ref: AKBC @ EMNLP, ACL, Oct 2026, Budapest, Hungary

Code: https://github.com/l-pommeret/graph-nli

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Interpretability — 19× in this paper · explained in 15 episodes
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Natural Language Inference — 8× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Knowledge Graph — 6× in this paper · explained in 7 episodes
• Knowledge graphs — 4× in this paper · explained in 2 episodes
• Natural Language Inference (NLI) — 4× in this paper · explained in 2 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Natural Language Understanding — 3× in this paper
• Graph Construction — 2× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• While Large Language Model — 2× in this paper
• Zero-Shot Transfer — 2× in this paper · explained in 3 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• In Question Answering — 1× in this paper
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Atomic-SNLI: Fine-Grained Natural Language Inference through Atomic Fact Decomposition
• RoBERTa: A Robustly Optimized BERT Pretraining Approach

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