  Current evaluations of large language models (LLMs) often overlook
non-determinism, typically focusing on a single output per example. This limits
our understanding of LLM performance variability in real-world applications.
Our study addresses this issue by exploring key questions about the performance
differences between greedy decoding and sampling, identifying benchmarks'
consistency regarding non-determinism, and examining unique model behaviors.
Through extensive experiments, we observe that greedy decoding generally
outperforms sampling methods for most evaluated tasks. We also observe
consistent performance across different LLM sizes and alignment methods, noting
that alignment can reduce sampling variance. Moreover, our best-of-N sampling
approach demonstrates that smaller LLMs can match or surpass larger models such
as GPT-4-Turbo, highlighting the untapped potential of smaller LLMs. This
research shows the importance of considering non-determinism in LLM evaluations
and provides insights for future LLM development and evaluation.
