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Mamta Mamta

2 accepted papers

2025

FactEval: Evaluating the Robustness of Fact Verification Systems in the Era of Large Language Models

NAACL 2025long

Whilst large language models (LLMs) have made significant advances in every natural language processing task, studies have shown that these models are vulnerable to small perturbations in the inputs, raising concerns about their robustness in the real-world. Given the rise of misinformation online a…

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2024

BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability

EMNLP 2024main

Toxic content detection plays a vital role in addressing the misuse of social media platforms to harm people or groups due to their race, gender or ethnicity. However, due to the nature of the datasets, systems develop an unintended bias due to the over-generalization of the model to the training da…