PredictaBoard introduces a novel benchmarking framework for evaluating how accurately we can predict when large language models will succeed or fail at tasks.

• Addresses unpredictable failures in LLMs, even on simple reasoning tasks
• Creates metrics to evaluate the reliability of score predictors
• Enables the identification of "safe zones" for LLM operation
• Provides a foundation for developing more reliable AI systems

This research is critical for security applications where unpredictable AI failures could have serious consequences. By improving our ability to anticipate when LLMs might fail, organizations can establish safer operational boundaries and implement appropriate safeguards.

PredictaBoard: Benchmarking LLM Score Predictability