Unverified50% confidenceFactExact time
Because the output of large language models is non-deterministic, the same input may produce different outputs, making it difficult to cover all edge cases with unit tests alone.
1
Sources
50%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge language models have inherent non-determinism — even with identical prompt inputs, the model may return results that differ in format, content, or even logic at different times.79% similarUnverified大语言模型本质上是概率性的,相同的输入可能产生不同的输出,这与传统软件的确定性行为截然不同74% similarUnverifiedOOD detection for autoregressive language models is more difficult than for traditional machine learning because the linguistic compositional space is vast, semantic OOD does not correspond to surface-form OOD, and adversaries can construct inputs that appear statistically in-distribution but semantically trigger OOD behavior.74% similarUnverifiedPerplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.71% similarUnverifiedLanguage model outputs are probabilistic and semantically ambiguous, making it difficult to measure quality with precise assertions, which poses challenges for migrating TDD concepts to the prompt engineering domain.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/869559API
curl https://kongchang.com/api/v1/knowledge/claims/869559MCP
get_claim(id=869559)