The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore
  Non-Determinism
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.
6695cf864c450902c8cf3e5a
Yifan Song
claimed_verified
2025-05-13T09:54:35.762Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1675437310060-61d595f0bfc2372aaf42b766.jpeg
Yifan Song
user
Solaris99
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Guoyin Wang
claimed_verified
2024-12-03T11:17:06.403Z
6490d4ba1afdee3acd1147f6
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Guoyin Wang
user
guoyinwang
6695cf864c450902c8cf3e5c
Sujian Li
6695cf864c450902c8cf3e5d
Bill Yuchen Lin
extracted_pending
2024-07-16T01:40:23.582Z
607f666a4ad99100d63ce35c
https://cdn-avatars.huggingface.co/v1/production/uploads/607f666a4ad99100d63ce35c/QxhxnvfeV6efkxwUFHwjI.png
Bill Yuchen Lin
user
yuchenlin
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore
  Non-Determinism
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.
Research explores non-determinism in LLM evaluations, comparing greedy decoding and sampling, and finds that smaller models can match larger ones with best-of-N sampling.
greedy decoding sampling benchmarks alignment methods best-of-N sampling LLM language models