AI Safety Research | Award-Winning Papers | Virtue AI

Research Insights

From award-winning academic research to production-grade AI safety.

Recognized by

Outstanding Paper | NeurIPS

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Best Paper | NeurIPS

When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?

Best Research Paper Finalist | PVLDB

LLM-PBE: Assessing Data Privacy in Large Language Models

Research Papers

AISTATS

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias

arXiv

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

ICML

HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

ICML

Fair Federated Learning via the Proportional Veto Core

ICML

Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing.

ICLR

Effective and Efficient Federated Tree Learning on Hybrid Data

ICML

Differentially Private Synthetic Data via Foundation Model APIs 2

NeurIPS

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

ICML

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

ICLR

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

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