We introduce MorphNLI, a modular step-by-step approach to natural language inference (NLI). When classifying the premise-hypothesis pairs into {entailment, contradiction, neutral}, we use a language model to generate the necessary edits to incrementally transform (i.e., morph) the premise into the hypothesis. Then, using an off-the-shelf NLI model we track how the entailment progresses with these atomic changes, aggregating these intermediate labels into a final output. We demonstrate the advantages of our proposed method particularly in realistic cross-domain settings, where our method always outperforms strong baselines with improvements up to 12.6% (relative). Further, our proposed approach is explainable as the atomic edits can be used to understand the overall NLI label.
View on arXiv@article{negru2025_2502.09567,
title={ MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing },
author={ Vlad Andrei Negru and Robert Vacareanu and Camelia Lemnaru and Mihai Surdeanu and Rodica Potolea },
journal={arXiv preprint arXiv:2502.09567},
year={ 2025 }
}