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Found 8 papers, 5 papers with code
Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading
A common approach to quantifying neural text classifier interpretability is to calculate faithfulness metrics based on iteratively masking salient input tokens and measuring changes in the model prediction.
Measuring Improvement of F$_1$-Scores in Detection of Self-Admitted Technical Debt
We find that our trained BERT model improves over the best performance of all previous methods in 19 of the 20 projects in cross-project scenarios.
In BLOOM: Creativity and Affinity in Artificial Lyrics and Art
We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers.
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems.
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns.
Towards Ethical Content-Based Detection of Online Influence Campaigns
The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development.
McDiarmid Drift Detection Methods for Evolving Data Streams
Accordingly, concept drifts need to be detected, and handled, as soon as possible.
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
In addition, a number of methods have been developed to detect concept drifts in these streams.
