conftrace_

safety evaluation

94 papers

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Co-occurring keywords

large language model (13587) red teaming (61) jailbreak attack (225) adversarial testing (31) language model (4599) adversarial attack (1621) ai safety (235) benchmark evaluation (1556) model safety (101) multimodal large language model (966)

Papers

Refusal-Aware Red Teaming: Exposing Inconsistency in Safety Evaluations EMNLP 2025
MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? EMNLP 2025
IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages EMNLP 2025
Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios EMNLP 2025
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints AAAI 2025
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding ACL 2025
Safer or Luckier? LLMs as Safety Evaluators Are Not Robust to Artifacts ACL 2025
PL-Guard: Benchmarking Language Model Safety for Polish ACL 2025
Six-CD: Benchmarking Concept Removals for Text-to-image Diffusion Models CVPR 2025
CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs EMNLP 2025
SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use NAACL 2025
GeoSAFE - A Novel Geospatial Artificial Intelligence Safety Assurance Framework and Evaluation for LLM Moderation AACL 2025
Fake Alignment: Are LLMs Really Aligned Well? NAACL 2024
T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models NIPS 2024
Realistic Evaluation of Toxicity in Large Language Models ACL 2024
GuardBench: A Large-Scale Benchmark for Guardrail Models EMNLP 2024
STAR: SocioTechnical Approach to Red Teaming Language Models EMNLP 2024
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models EMNLP 2024
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety ACL 2024
Safety of Multimodal Large Language Models on Images and Text IJCAI 2024
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models NIPS 2024
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities NAACL 2024
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors EMNLP 2024
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users NIPS 2024
SAFETY-J: Evaluating Safety with Critique EMNLP 2024