CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions
Fuente:
arXiv
Guardado en:
| Autores principales: | Levi, Matan, Alluouche, Yair, Ohayon, Daniel, Puzanov, Anton |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Toward Cybersecurity-Expert Small Language Models
por: Levi, Matan, et al.
Publicado: (2025)
por: Levi, Matan, et al.
Publicado: (2025)
AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
por: Cheng, Yi, et al.
Publicado: (2024)
por: Cheng, Yi, et al.
Publicado: (2024)
ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
por: Huang, Yue, et al.
Publicado: (2025)
por: Huang, Yue, et al.
Publicado: (2025)
Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
por: Levi, Elad, et al.
Publicado: (2024)
por: Levi, Elad, et al.
Publicado: (2024)
Cyber-Zero: Training Cybersecurity Agents without Runtime
por: Zhuo, Terry Yue, et al.
Publicado: (2025)
por: Zhuo, Terry Yue, et al.
Publicado: (2025)
Glider: Global and Local Instruction-Driven Expert Router
por: Li, Pingzhi, et al.
Publicado: (2024)
por: Li, Pingzhi, et al.
Publicado: (2024)
Lightweight Large Language Model for Medication Enquiry: Med-Pal
por: Elangovan, Kabilan, et al.
Publicado: (2024)
por: Elangovan, Kabilan, et al.
Publicado: (2024)
Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach
por: Mokhtarabadi, Hojjat, et al.
Publicado: (2024)
por: Mokhtarabadi, Hojjat, et al.
Publicado: (2024)
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
por: Zhang, Xinghua, et al.
Publicado: (2024)
por: Zhang, Xinghua, et al.
Publicado: (2024)
Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak
por: Du, Yanrui, et al.
Publicado: (2023)
por: Du, Yanrui, et al.
Publicado: (2023)
ExpertSteer: Intervening in LLMs through Expert Knowledge
por: Wang, Weixuan, et al.
Publicado: (2025)
por: Wang, Weixuan, et al.
Publicado: (2025)
Prompt Repetition Improves Non-Reasoning LLMs
por: Leviathan, Yaniv, et al.
Publicado: (2025)
por: Leviathan, Yaniv, et al.
Publicado: (2025)
Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?
por: Klievtsova, Nataliia, et al.
Publicado: (2023)
por: Klievtsova, Nataliia, et al.
Publicado: (2023)
Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features
por: Tshimula, Jean Marie, et al.
Publicado: (2024)
por: Tshimula, Jean Marie, et al.
Publicado: (2024)
Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
por: Bai, Jun, et al.
Publicado: (2025)
por: Bai, Jun, et al.
Publicado: (2025)
Mixture of Experts for Low-Resource LLMs
por: Joseph, Ori Bar, et al.
Publicado: (2026)
por: Joseph, Ori Bar, et al.
Publicado: (2026)
FLAME: Empowering Frozen LLMs for Knowledge Graph Completion
por: Xue, Bo, et al.
Publicado: (2024)
por: Xue, Bo, et al.
Publicado: (2024)
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following Capability
por: wang, Jiaming, et al.
Publicado: (2025)
por: wang, Jiaming, et al.
Publicado: (2025)
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
por: Guo, Hongcheng, et al.
Publicado: (2025)
por: Guo, Hongcheng, et al.
Publicado: (2025)
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
por: Zhu, Tong, et al.
Publicado: (2024)
por: Zhu, Tong, et al.
Publicado: (2024)
Instruction Agent: Enhancing Agent with Expert Demonstration
por: Li, Yinheng, et al.
Publicado: (2025)
por: Li, Yinheng, et al.
Publicado: (2025)
ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
por: Hu, Yutong, et al.
Publicado: (2024)
por: Hu, Yutong, et al.
Publicado: (2024)
Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning
por: Cui, Sijia, et al.
Publicado: (2025)
por: Cui, Sijia, et al.
Publicado: (2025)
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
por: Gekhman, Zorik, et al.
Publicado: (2024)
por: Gekhman, Zorik, et al.
Publicado: (2024)
Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering
por: Chen, Yuxin, et al.
Publicado: (2026)
por: Chen, Yuxin, et al.
Publicado: (2026)
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
por: Zhou, Yixiao, et al.
Publicado: (2025)
por: Zhou, Yixiao, et al.
Publicado: (2025)
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
por: Sukhbaatar, Sainbayar, et al.
Publicado: (2024)
por: Sukhbaatar, Sainbayar, et al.
Publicado: (2024)
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
por: Shaham, Uri, et al.
Publicado: (2024)
por: Shaham, Uri, et al.
Publicado: (2024)
The Instruction Gap: LLMs get lost in Following Instruction
por: Tripathi, Vishesh, et al.
Publicado: (2025)
por: Tripathi, Vishesh, et al.
Publicado: (2025)
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
por: Zhang, Zhexin, et al.
Publicado: (2024)
por: Zhang, Zhexin, et al.
Publicado: (2024)
Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
por: Zhang, Junyan, et al.
Publicado: (2025)
por: Zhang, Junyan, et al.
Publicado: (2025)
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
por: Arroyo, Alberto Mario Ceballos, et al.
Publicado: (2024)
por: Arroyo, Alberto Mario Ceballos, et al.
Publicado: (2024)
Instruction-tuning Aligns LLMs to the Human Brain
por: Aw, Khai Loong, et al.
Publicado: (2023)
por: Aw, Khai Loong, et al.
Publicado: (2023)
An Expert-grounded benchmark of General Purpose LLMs in LCA
por: Donaldson, Artur, et al.
Publicado: (2025)
por: Donaldson, Artur, et al.
Publicado: (2025)
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
por: Wang, Yejie, et al.
Publicado: (2024)
por: Wang, Yejie, et al.
Publicado: (2024)
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs
por: Jindal, Ishan, et al.
Publicado: (2024)
por: Jindal, Ishan, et al.
Publicado: (2024)
The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple, Self-Contained Directives
por: Lim, Henry, et al.
Publicado: (2025)
por: Lim, Henry, et al.
Publicado: (2025)
AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications
por: Toney-Wails, Autumn, et al.
Publicado: (2024)
por: Toney-Wails, Autumn, et al.
Publicado: (2024)
CoME: Empowering Channel-of-Mobile-Experts with Informative Hybrid-Capabilities Reasoning
por: Liu, Yuxuan, et al.
Publicado: (2026)
por: Liu, Yuxuan, et al.
Publicado: (2026)
Empowering LLMs with Logical Reasoning: A Comprehensive Survey
por: Cheng, Fengxiang, et al.
Publicado: (2025)
por: Cheng, Fengxiang, et al.
Publicado: (2025)
Ejemplares similares
-
Toward Cybersecurity-Expert Small Language Models
por: Levi, Matan, et al.
Publicado: (2025) -
AutoPal: Autonomous Adaptation to Users for Personal AI Companionship
por: Cheng, Yi, et al.
Publicado: (2024) -
ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
por: Huang, Yue, et al.
Publicado: (2025) -
Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
por: Levi, Elad, et al.
Publicado: (2024) -
Cyber-Zero: Training Cybersecurity Agents without Runtime
por: Zhuo, Terry Yue, et al.
Publicado: (2025)