1 citations · 2 across the 4 of their papers we have counts for
5 papers
AdaGuard: Enhancing Safety and Policy Compliance with Reasoning-Enabled LLM-As-A-Judge Guardrails
Melissa Kazemi Rad, Sihui Dai, Isha Slavin +6
Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guard…
DynaGuard: A Dynamic Guardian Model With User-Defined Policies
Monte Hoover, Vatsal Baherwani, Neel Jain +7
Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard…
GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection
Melissa Kazemi Rad, Alberto Purpura, Himanshu Kumar +2
We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a n…
Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models
Alberto Purpura, Sahil Wadhwa, Jesse Zymet +5
The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems ag…
Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment
Melissa Kazemi Rad, Huy Nghiem, Andy Luo +3
Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to e…