Papers with detection effectiveness

2 papers
A Robust Semantics-based Watermark for Large Language Model against Paraphrasing (2024.findings-naacl)

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Challenge: Existing methods to detect LLM-generated content use simple hashes of precedent tokens to partition vocabulary.
Approach: They propose a semantics-based watermark framework to enhance the robustness against paraphrase.
Outcome: The proposed framework is robust under different paraphrases and the semantic meaning of the sentences will be likely preserved under paraphrase.
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

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Challenge: Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach: They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome: The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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