VertMark embeds robust, training-free watermarks into vertical domain language models by creating hidden semantic equivalence between low-frequency triggers and high-frequency domain terms via parameter swaps, supporting reliable verification with negligible performance impact.
Sentence-bert: Sentence embeddings using siamese bert-networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.
citing papers explorer
-
VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models
VertMark embeds robust, training-free watermarks into vertical domain language models by creating hidden semantic equivalence between low-frequency triggers and high-frequency domain terms via parameter swaps, supporting reliable verification with negligible performance impact.
-
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.