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Found 6 papers, 1 papers with code
How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study
We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time.
Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications
Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale.
Self-training with Few-shot Rationalization
While pre-trained language models have obtained state-of-the-art performance for several natural language understanding tasks, they are quite opaque in terms of their decision-making process.
Investigating Answerability of LLMs for Long-Form Question Answering
As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences.
Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU
While pre-trained language models have obtained state-of-the-art performance for several natural language understanding tasks, they are quite opaque in terms of their decision-making process.
Fake News Detection via NLP is Vulnerable to Adversarial Attacks
In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news.
