Papers by Tyler Cody
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for estimation of uncertainty overlook semantic dependencies, authors say . genUINE: Graph ENhanced mUlti-level uncertainty Estimation for Large Language Models leverages dependency parse trees and hierarchical graph pooling . |
| Approach: | They propose a graph-enhanced mUlti-level uncertaINty estimation framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. |
| Outcome: | The proposed framework achieves higher AUROC and lower calibration errors than existing methods. |
From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been explored for automating or enhancing penetration testing tasks, but their effectiveness and reliability remain open questions. |
| Approach: | They evaluate multiple LLM-based agents across realistic penetration testing scenarios . they also examine impact of core functional capabilities on agent success . |
| Outcome: | The proposed models improve agent performance in multi-step and real-time penetration testing scenarios. |