LLM 蕴含多重性：部署上下文如何重塑模型层面的偏好与价值观
LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
AI总结：
本研究通过两个成对比较范式（国家偏好排序和效用判断）发现，部署上下文（如撰写Reddit帖子或新闻文章）对LLM偏好和价值观的影响远大于提示改写和温度控制，表明模型层面的偏好是上下文依赖的。
摘要：
大型语言模型（LLM）在最近的评估工作中越来越被描述为具有稳定的、模型层面的偏好和价值观系统。然而，伴随的稳健性检查仅限于偶然的提示扰动，如句法变化和选项重新排序。这留下了当周围任务上下文发生变化时（如大多数实际部署中那样），所测量的属性是否仍然存在的问题。我们直接在两个已建立的成对范式中对此进行测试：国家偏好排序和效用判断。在两者中，我们将部署上下文——模型在做出具体价值依赖选择时执行的高层任务——作为我们的控制变量，在不同框架（如撰写Reddit帖子或新闻文章）之间变化。在五个LLM和超过120万个成对决策中，部署上下文产生的变化远大于提示释义和温度控制。在15个国家的偏好排序中，上下文引发了广泛的、统计上显著的排名变化；先前工作中报告的总体全球北方偏好本身是上下文依赖的，每个模型的偏见在不同上下文中系统性变化。在50个结果的效用引出中，跨类别的广泛排序得以保留，但领域内的细粒度排名变化很大，并且结果之间的基数交换率（例如，一个地区的多少条生命等于另一个地区的一条生命）在中位数上变化了2.47倍。因此，报告的模型层面偏好和效用最好被理解为上下文条件下的测量，而不是固定的模型属性：在一种框架下获得的安全保证在另一种框架下提供的保证有限。
英文摘要：
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.
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