  Language models (LMs) are becoming the foundation for almost all major
language technologies, but their capabilities, limitations, and risks are not
well understood. We present Holistic Evaluation of Language Models (HELM) to
improve the transparency of language models. First, we taxonomize the vast
space of potential scenarios (i.e. use cases) and metrics (i.e. desiderata)
that are of interest for LMs. Then we select a broad subset based on coverage
and feasibility, noting what's missing or underrepresented (e.g. question
answering for neglected English dialects, metrics for trustworthiness). Second,
we adopt a multi-metric approach: We measure 7 metrics (accuracy, calibration,
robustness, fairness, bias, toxicity, and efficiency) for each of 16 core
scenarios when possible (87.5% of the time). This ensures metrics beyond
accuracy don't fall to the wayside, and that trade-offs are clearly exposed. We
also perform 7 targeted evaluations, based on 26 targeted scenarios, to analyze
specific aspects (e.g. reasoning, disinformation). Third, we conduct a
large-scale evaluation of 30 prominent language models (spanning open,
limited-access, and closed models) on all 42 scenarios, 21 of which were not
previously used in mainstream LM evaluation. Prior to HELM, models on average
were evaluated on just 17.9% of the core HELM scenarios, with some prominent
models not sharing a single scenario in common. We improve this to 96.0%: now
all 30 models have been densely benchmarked on the same core scenarios and
metrics under standardized conditions. Our evaluation surfaces 25 top-level
findings. For full transparency, we release all raw model prompts and
completions publicly for further analysis, as well as a general modular
toolkit. We intend for HELM to be a living benchmark for the community,
continuously updated with new scenarios, metrics, and models.
