CSO-LLM proposes class subspace orthogonalization to enhance post-training backdoor detection sensitivity/specificity and enable accurate trigger inversion in LLMs via continuous embedding optimization and discrete greedy accretion.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs
CSO-LLM proposes class subspace orthogonalization to enhance post-training backdoor detection sensitivity/specificity and enable accurate trigger inversion in LLMs via continuous embedding optimization and discrete greedy accretion.