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Paper

Enhancing adversarial robustness in Natural Language Inference using explanations

by Independent / Community 02119b8cb09ee755f582a5f54f2728f45f9b9aac

Free2AITools Nexus Index

63.2

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70

P: Popularity 45

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

The surge of state-of-the-art transformer-based models has undoubtedly pushed the limits of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the underexplored task of Natural Language Inference (NLI), since models trained on popular well-suited datasets are susceptible to adversarial attacks, allowing subtle input interventions to mislead the model. In this work, we validate the usage of natural language explanation as a model-agnostic defence strategy through ...

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Semantic Scholar 4 Citations

Paper Information Summary
Entity Passport
Registry ID	02119b8cb09ee755f582a5f54f2728f45f9b9aac
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{02119b8cb09ee755f582a5f54f2728f45f9b9aac,
  author = {Unknown},
  title = {Enhancing adversarial robustness in Natural Language Inference using explanations Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02119b8cb09ee755f582a5f54f2728f45f9b9aac}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Enhancing adversarial robustness in Natural Language Inference using explanations [Paper]. Free2AITools. https://api.semanticscholar.org/02119b8cb09ee755f582a5f54f2728f45f9b9aac

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70

Popularity (P) 45

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Enhancing adversarial robustness in Natural Language Inference using explanations: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"The surge of state-of-the-art transformer-based models has undoubtedly pushed the limits of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the underexplored task of Natural Language Inference (NLI), since models trained on popular well-suited datasets are susceptible to adversarial attacks, allowing subtle input interventions to mislead the model. In this work, we validate the usage of natural language explanation as a model-agnostic defence strategy through ..."

❝ Cite Node

@article{Unknown2026Enhancing,
  title={Enhancing adversarial robustness in Natural Language Inference using explanations},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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Read the full paper on arXiv

📊 Research Signals

📈4CitationsSemantic Scholar

🏛️70AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️infrastructure opsField

🏷️ Research Topics

transformer architecture

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 4

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