Large language models (LLMs) are increasingly characterised in recent evaluation work as having stable, model-level preference and value systems. However, accompanying robustness checks are limited to incidental prompt perturbations such as syntax variation and option reordering. This leaves open whether the measured properties survive when the surrounding task context changes, as it does in most real deployments. We test this directly across two established pairwise paradigms: ranking country preferences and eliciting utility judgements. In both, we make the deployment context -- the high-level task the model is performing while making concrete value-dependent choices -- our controlled variable, varied across framings such as writing a Reddit post or a news article. Across five LLMs and over 1.2M pairwise decisions, deployment context produces variation far larger than prompt paraphrasing and temperature controls. In country preference rankings over 15 countries, context induces widespread, statistically significant rank shifts; the aggregate Global North favouritism reported in prior work is itself context-dependent, with each model's bias shifting systematically across contexts. In utility elicitation over 50 outcomes, broad cross-category ordering is preserved, but fine-grained rankings within domains vary substantially, and cardinal exchange rates between outcomes (e.g. how many lives in one region equal one in another) shift by a factor of 2.47 at the median. Reported model-level preferences and utilities are therefore better understood as context-conditioned measurements than fixed model-level properties: safety guarantees obtained under one framing provide limited assurance in another.

翻译：大型语言模型（LLM）在近期评估工作中日益被描述为具有稳定的模型层面偏好与价值系统。然而，伴随的稳健性检验局限于偶然性提示扰动，如句法变换和选项重排。这使得待验证的测量属性在真实部署场景中常见的任务语境变化下是否依然成立尚不明确。我们通过两个成熟的成对比较范式（国家偏好排序与效用判断诱发）对此进行了直接检验。在两个范式中，我们将部署语境（模型在执行具体价值相关选择时进行的高层任务）作为可控变量，使其在诸如撰写Reddit帖文或新闻稿等不同框架间变化。在五个LLM和超过120万次成对决策中，部署语境所产生的变化幅度远大于提示释义和温度系数控制。在涉及15个国家的偏好排序中，语境引发了广泛且统计显著的排位迁移；既往研究中所报告的全局性"全球北方"偏好本身也依赖于语境——每个模型的偏差在不同语境间呈现系统性转移。在针对50项结果进行的效用诱发中，跨类别的广义排序得以保持，但领域内的精细排行差异显著；结果之间的基数交换率（例如某地区一条生命等价于另一地区多少条生命）在中位数上产生了2.47倍的变化。因此，所报告的模型层面偏好与效用更应被理解为条件化语境测量结果，而非固定的模型特性：在某一框架下获得的安全性保障在另一框架中提供的保证有限。