Metrics to Meaning: Enabling Human-Interpretable Language Model Assessment

As language models grow more advanced and pervasive, existing benchmark-driven evaluation paradigms are insufficient to characterize and audit model strengths, limitations, biases, and potential harms. Corpora-based testing and metrics like accuracy offer little transparency or human insight into model behaviors. This paper puts forth a comprehensive framework for enabling diverse stakeholders-from developers, researchers, and regulators to end-users and subjects of model outputs-to interpret, trust, and responsibly advance language models. The multidimensional methodology elevates model transparency through interactive questionnaires that systematically probe capabilities. Explainability interfaces powered by state-ofthe-art algorithms demystify model reasoning behind outputs. Auditing workflows tailored for accessibility allow stakeholders to rigorously scrutinize models, surface biases, and illuminate blindspots augmented by public feedback tools. Together these human-centered instrumentation equip varied stakeholders to jointly advance robust, ethical and accountable language technologies. While focused on language, this paradigm of stakeholder participation paves a promising path for interpretable and trustworthy AI systems that serve broad public interests.