stanford-crfm/helm

HELM is the academic gold standard for 'independent' evaluation of foundation models. Its defensibility is not just in the code, but in its institutional credibility (Stanford CRFM) and the 'data gravity' of its historical results leaderboard. With over 2,700 stars and significant forks, it has high adoption among researchers and policy-makers (including influence on NIST). While EleutherAI's 'lm-evaluation-harness' is more frequently used for raw 'accuracy' leaderboards (like Open LLM Leaderboard), HELM's 'holistic' approach—incorporating toxicity, fairness, and efficiency—makes it more resilient to being replaced by simple benchmark scripts. Frontier labs are unlikely to displace this because they need third-party validation to avoid conflict-of-interest claims; they are more likely to submit models to HELM than build a competitor. The primary threat comes from cloud providers (AWS Bedrock, Google Vertex) integrating similar 'Model Evaluation' suites into their platforms to capture enterprise users who don't want to manage their own evaluation infrastructure.