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Found 9 papers, 2 papers with code
GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer
Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors.
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations
State-of-the-art machine learning models are prone to adversarial attacks”:" Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence.
SHAP-Based Explanation Methods: A Review for NLP Interpretability
Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models.
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
As hate speech spreads on social media and online communities, research continues to work on its automatic detection.
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks
Adversarial attacks are a major challenge faced by current machine learning research.
Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers
Large-scale surveys are a widely used instrument to collect data from a target audience.
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?
Text simplification seeks to improve readability while retaining the original content and meaning.
IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications.
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
Adversarial attacks are a major challenge faced by current machine learning research.
