NIST Seeks Input on Draft AI Benchmark Evaluation Guidance
The National Institute of Standards and Technology is inviting feedback from industry, government, and research stakeholders on a new framework designed to improve how language models are evaluated through automated benchmarking. The agency’s Center for AI Standards and Innovation released an initial public draft called AI 800-2, “Practices for Automated Benchmark Evaluations of Language Models,” with comments welcome through March 31.
Automated benchmark evaluations are increasingly used to support AI procurement and deployment, particularly when resources or timelines are tight. However, benchmarks aren’t a one-size-fits-all solution. There is growing concern that while benchmark tests are essential tools, there is still little agreement on standards that ensure results are valid, reproducible, and transparent.
The draft centers on three main areas: defining what to evaluate and selecting benchmarks; carrying out the evaluations; and analyzing and reporting results. It notes benchmarks work best when tasks are structured, verifiable, and stable over time, but they are less effective for subjective judgments, dynamic tasks, or scenarios involving human input.
A core recommendation is to start by clearly documenting the goal of the evaluation and how the results will be used. Evaluators should define both the intended use of the measurements and the underlying capability or construct being assessed. Organizations are urged to document what each benchmark measures and whether it directly aligns with the evaluation goal or merely serves as a proxy.
Beyond choosing benchmarks, the guidance emphasizes the design of evaluation protocols—the concrete procedures that shape results. It outlines emerging principles, including caution about providing internet access during testing, as it can introduce contamination and undermine benchmark integrity.
Stronger norms around statistical analysis and reporting are encouraged. Evaluators should quantify uncertainty with confidence intervals or standard errors rather than treating scores as absolute. Qualified conclusions are recommended, with care not to generalize findings beyond the intended scope.
The draft reinforces CAISI’s expanding role as the federal government’s primary hub for testing cutting‑edge AI models. Recent initiatives under the center include efforts to bring in experts for national security risk evaluations, AI red-teaming, and secure deployment guidance as part of a broader national AI action plan.
In addition, authorities are seeking industry input on security risks and safeguards for agentic AI systems, pointing to potential threats such as backdoor exposures and data poisoning.
