Opinion
Deep Learning with Yacine on MSNOpinion

Can you trust an LLM judge? An RL researcher's take

Explore the challenges and possibilities of using large language models as judges. An RL researcher breaks down reliability, bias, and how AI evaluates decisions. #LLM #AIResearch #ReinforcementLearni ...
Infosecurity Magazine

Researchers Discover Major Security Gaps in LLM Guardrails

Palo Alto Networks’ Unit 42 has developed a successful attack to bypass safety guardrails in popular generative AI tools ...
InfoWorld

Databricks adds MemAlign to MLflow to cut cost and latency of LLM evaluation

Databricks’ Mosaic AI Research team has added a new framework, MemAlign, to MLflow, its managed machine learning and generative AI lifecycle development service. MemAlign is designed to help ...
WSPA

Root Signals Introduces Root Judge: The State-of-the-Art Judge Model for Measuring the Reliability of LLM Applications

Root Judge is a powerful mid-sized LLM that enables reliable and customizable LLM system evaluations. It is trained specifically for evaluation: LLM-as-a-Judge. With solutions for reliable and ...
Communications of the ACM

LLM Evaluation is Key to Accurate, Reliable, Effective GenAI

Enter large language model (LLM) evaluation. The purpose of LLM evaluation is to analyze and refine GenAI outputs to improve their accuracy and reliability while avoiding bias. The evaluation process ...