The increasing versatility of language models LMs has given rise to a new
class of benchmarks that comprehensively assess a broad range of capabilities.
Such benchmarks are associated with massive computational costs reaching
thousands of GPU hours per model. However the efficiency aspect of these
evaluation efforts had raised little discussion in the literature. In this work
we present the problem of Efficient Benchmarking namely intelligently reducing
the computation costs of LM evaluation without compromising reliability. Using
the HELM benchmark as a test case we investigate how different benchmark design
choices affect the computation-reliability tradeoff. We propose to evaluate the
reliability of such decisions by using a new measure Decision Impact on
Reliability DIoR for short. We find for example that the current leader on HELM
may change by merely removing a low-ranked model from the benchmark and observe
that a handful of examples suffice to obtain the correct benchmark ranking.
Conversely a slightly different choice of HELM scenarios varies ranking widely.
Based on our findings we outline a set of concrete recommendations for more
efficient benchmark design and utilization practices leading to dramatic cost
savings with minimal loss of benchmark reliability often reducing computation
by x100 or more