Towards Efficient Threat Intelligence Instruction Tuning via Quality-Aware and Optimal Transport-Guided Data Selection
CTI-TDS selects compact, high-quality instruction data for threat-intelligence model tuning.
Researchers propose CTI-TDS, a targeted data-selection method for instruction tuning in cyber threat intelligence analysis. It filters candidate instructions using style consistency and security-knowledge verification, then applies entropic optimal transport in a learned gradient subspace to select diverse samples. Experiments on CTIBench show that fine-tuning multiple large language models on a small high-value subset outperforms existing selection methods.
- CTI-TDS filters instruction data by style and security-knowledge checks
- Optimal transport aligns sample and target-task gradient distributions
- Tests use CTIBench and multiple large language models
- Small selected datasets beat existing selection methods
From the original article
1 min read at arxiv.orgHigh-quality data is essential for improving instruction tuning in threat intelligence analysis. However, existing methods for generating cybersecurity instruction data lack reliable quality assessment mechanisms, and gradient influence-based data selection methods primarily focus on single-sample matching, failing to cover diverse and complex security capabilities.
By Jiyu Tian · arxiv.org
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