Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass

• URL: http://arxiv.org/abs/2509.18901v1
• Date: Tue, 23 Sep 2025 11:30:42 GMT
• Title: Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass
• Authors: Nicholas Popovič, Michael Färber,
• Abstract summary: JEDI is an encoder-only architecture that jointly performs atomic fact decomposition and interpretable inference.<n>To facilitate training, we produce a large corpus of synthetic rationales covering multiple NLI benchmarks.<n>Our findings show that interpretability and robust generalization in NLI can be realized using encoder-only architectures and synthetic rationales.
• Score: 4.990228412613982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works in Natural Language Inference (NLI) and related tasks, such as automated fact-checking, employ atomic fact decomposition to enhance interpretability and robustness. For this, existing methods rely on resource-intensive generative large language models (LLMs) to perform decomposition. We propose JEDI, an encoder-only architecture that jointly performs extractive atomic fact decomposition and interpretable inference without requiring generative models during inference. To facilitate training, we produce a large corpus of synthetic rationales covering multiple NLI benchmarks. Experimental results demonstrate that JEDI achieves competitive accuracy in distribution and significantly improves robustness out of distribution and in adversarial settings over models based solely on extractive rationale supervision. Our findings show that interpretability and robust generalization in NLI can be realized using encoder-only architectures and synthetic rationales. Code and data available at https://jedi.nicpopovic.com

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