Abstract
The output of large language models (LLM) is unstable, due to both non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distributional shifts in outputs, it is largely assumed, yet unexplored, that the prompt brittleness effect is unique to LLMs. This raises the question: do human annotators show similar sensitivity to instruction changes? If so, should prompt brittleness in LLMs be considered problematic? One may alternatively hypothesize that prompt brittleness correctly reflects human annotation variances. To fill this research gap, we systematically compare the effects of prompt modifications on LLMs and identical instruction modifications for human annotators, focusing on the question of whether humans are similarly sensitive to prompt perturbations. To study this, we prompt both humans and LLMs for a set of text classification tasks conditioned on prompt variations. Our findings indicate that both humans and LLMs exhibit increased brittleness in response to specific types of prompt modifications, particularly those involving the substitution of alternative label sets or label formats. However, the distribution of human judgments is less affected by typographical errors and reversed label order than that of LLMs.
Abstract (translated)
大型语言模型（LLM）的输出不稳定，这既由于解码过程中的非确定性，也由于提示的脆弱性。虽然LLM生成内在的非确定性可以通过输出分布的变化模拟现有标注中的不确定性，但人们普遍认为但尚未充分探讨的是，提示脆弱性的效果是特定于大型语言模型的独特现象。这就提出了一个问题：人类注释者对指令变化是否表现出类似的敏感度？如果是的话，那么大型语言模型中的提示脆性是否应被视为问题？另一种假设可能是，提示脆性准确反映了人类标注的变化。为填补这一研究空白，我们系统地比较了对LLM和人类注释者的相同指令修改的提示更改效果，并重点关注人类是否同样敏感于提示扰动的问题。为此，我们在一系列文本分类任务中分别向人类和大型语言模型提出了根据提示变化条件的任务。我们的发现表明，无论是人类还是大型语言模型，在面对特定类型的提示更改时都会表现出更大的脆弱性，尤其是那些涉及替代标签集或标签格式替换的情况。然而，与LLM相比，人类判断的分布受到拼写错误和标签顺序反转的影响较小。
URL
https://arxiv.org/abs/2509.07869
