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Found 12 papers, 4 papers with code
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes.
LLM-based NLG Evaluation: Current Status and Challenges
Evaluating natural language generation (NLG) is a vital but challenging problem in artificial intelligence.
On the Zero-Shot Generalization of Machine-Generated Text Detectors
The rampant proliferation of large language models, fluent enough to generate text indistinguishable from human-written language, gives unprecedented importance to the detection of machine-generated text.
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow
This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single lightweight framework.
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks
The results show that summaries generated by fine-tuned models lead to higher consistency in usefulness across all three tasks, as rankings of fine-tuned summarization systems are close across downstream tasks according to the proposed extrinsic metrics.
Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images
By stacking various layers of CSA blocks, we propose the Fourier Complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners.
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones.
Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues
We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers{'} output, and also use these methods in combination.
A Simple Approach to Sparse Clustering
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes.
