mlsanityData to 14 Oct 2026

MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing

arXiv 2502.09567 ↗First posted 13 Feb 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 47 references that are in the map. First posted on arXiv 13 Feb 2025.

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25 of 47 references.

References · 25

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13 more
  1. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
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  13. 2018Text Morphingcited by 2

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selectioncited by 4
  2. 2025SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Modelscited by 0