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AI Research
arXiv CS.AI·1d ago·5 min read
Unconstrained CTI-REALM with Open-Weights
TL;DR
Open-weight models can match the performance of expensive closed models in threat intelligence tasks at a fraction of the cost, given sufficient interaction turns. This study evaluates 26 models, revealing that open-weight models excel when message limits are relaxed.
✦ Why It Matters
Consider adopting open-weight models for threat intelligence tasks to reduce costs while maintaining performance.
Key Takeaways
- Evaluate open-weight models for cost-effective threat intelligence solutions.
- Relax message limits to fully assess model capabilities.
- Use stage-level metrics to optimize model performance in workflows.
- Analyze token generation to understand model efficiency.
How It Works
The study utilizes CTI-REALM, where agents query a telemetry store to transform threat intelligence reports into actionable detections. By evaluating models under a common framework, it identifies that open-weight models can perform comparably to closed models when given sufficient interaction turns.
The introduction of stage-level metrics allows for a more granular assessment of model performance, enabling task routing to the most capable model at each stage.