ReVD uses synthetic vulnerability reasoning data and curriculum preference optimization to boost LLM vulnerability detection accuracy by 12-22% over prior baselines on PrimeVul and SVEN.
In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data
ReVD uses synthetic vulnerability reasoning data and curriculum preference optimization to boost LLM vulnerability detection accuracy by 12-22% over prior baselines on PrimeVul and SVEN.