Shaping AI Benchmarks with Together AI Co-Founder Percy Liang

Benchmarking large language models requires a holistic approach that extends beyond simple accuracy metrics to include bias, robustness, and calibration. Percy Liang, an associate professor at Stanford and co-founder of TogetherAI, developed the Holistic Evaluation of Language Models (HELM) to provide a standardized, reproducible framework for assessing foundation models. While leaderboards drive innovation, they often incentivize overfitting, necessitating more transparent evaluation methodologies and task-specific testing. The shift toward open-source models fosters greater plurality in values and developer control, yet significant challenges persist regarding training data attribution and security. Agents represent the next frontier, though current limitations in long-range planning and inherent security vulnerabilities hinder their real-world deployment. Integrating human-in-the-loop systems and domain-specific evaluation remains essential for building reliable, scalable AI applications that transcend general-purpose capabilities.