SCOUT uses token saliency analysis to detect both standard and contextually-plausible backdoor attacks in language models while maintaining clean accuracy.
Next-generation phishing: How llm agents empower cyber attackers
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
LLMs tasked with allocating childhood lead testing resources in Chicago, New York, and DC overlooked high-prevalence neighborhoods and reached only 0.46 average accuracy despite marketed research capabilities.
citing papers explorer
-
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
SCOUT uses token saliency analysis to detect both standard and contextually-plausible backdoor attacks in language models while maintaining clean accuracy.
-
Can LLMs Help Allocate Public Health Resources? A Case Study on Childhood Lead Testing
LLMs tasked with allocating childhood lead testing resources in Chicago, New York, and DC overlooked high-prevalence neighborhoods and reached only 0.46 average accuracy despite marketed research capabilities.