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Found 3 papers, 2 papers with code
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
However, very few of these detectors are evaluated on shared benchmark datasets and even when they are, the datasets used for evaluation are insufficiently challenging -- lacking variations in sampling strategy, adversarial attacks, and open-source generative models.
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability.
Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.
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