2 citations · 3 across the 5 of their papers we have counts for
6 papers
A Systematic Approach for Large Language Models Debugging
Basel Shbita, Anna Lisa Gentile, Bing Zhang +10
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging…
STRIDE: A Systematic Framework for Selecting AI Modalities -- Agentic AI, AI Assistants, or LLM Calls
Shubhi Asthana, Bing Zhang, Chad DeLuca +2
The rapid shift from stateless large language models (LLMs) to autonomous, goal-driven agents raises a central question: When is agentic AI truly necessary? While agents enable mul…
OneShield -- the Next Generation of LLM Guardrails
Chad DeLuca, Anna Lisa Gentile, Shubhi Asthana +7
The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safe…
Adaptive PII Mitigation Framework for Large Language Models
Shubhi Asthana, Ruchi Mahindru, Bing Zhang +1
Artificial Intelligence (AI) faces growing challenges from evolving data protection laws and enforcement practices worldwide. Regulations like GDPR and CCPA impose strict complianc…
Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications
Shubhi Asthana, Bing Zhang, Ruchi Mahindru +3
The adoption of Large Language Models (LLMs) has revolutionized AI applications but poses significant challenges in safeguarding user privacy. Ensuring compliance with privacy regu…
Enterprise Benchmarks for Large Language Model Evaluation
Bing Zhang, Mikio Takeuchi, Ryo Kawahara +5
The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise a…