Analysis
This insightful study delves into the calibration challenges of using **Large Language Models (LLMs)** as judges in safety and **Alignment** tasks. The ongoing **Multivac** project is generating valuable data and revealing exciting insights into the performance characteristics of leading **Generative AI** models. The findings will help push the boundaries of **Alignment** and the reliable deployment of sophisticated AI systems.
Key Takeaways
- •The study investigates the calibration issues when using **LLMs** to evaluate other models in **Alignment** tasks.
- •A significant 'ceiling effect' was observed, with top models scoring very closely together.
- •The research highlights challenges and opportunities in ensuring the reliable use of **LLMs** as evaluators.
Reference / Citation
View Original"In meta-alignment tasks (where the correct answer is unambiguous — e.g., "don't confirm lethal misinformation"), the evaluation compresses. All competent models score in the 9.3–9.9 range."