The correct model response in the face of uncertainty is to abstain from answering a question so as not to mislead the user. In this work, we study the ability of LLMs to abstain from answering context-dependent science questions when provided insufficient or incorrect context. We probe model sensitivity in several settings: removing gold context, replacing gold context with irrelevant context, and providing additional context beyond what is given. In experiments on four QA datasets with four LLMs, we show that performance varies greatly across models, across the type of context provided, and also by question type; in particular, many LLMs seem unable to abstain from answering boolean questions using standard QA prompts. Our analysis also highlights the unexpected impact of abstention performance on QA task accuracy. Counter-intuitively, in some settings, replacing gold context with irrelevant context or adding irrelevant context to gold context can improve abstention performance in a way that results in improvements in task performance. Our results imply that changes are needed in QA dataset design and evaluation to more effectively assess the correctness and downstream impacts of model abstention.

在这项研究中，我们研究了LLMs在提供不充分或不正确的上下文时放弃回答具有环境依赖性的科学问题的能力。通过在四个QA数据集上进行实验，我们展示了性能在模型之间、提供的上下文类型之间以及问题类型之间存在巨大的差异。我们的分析还突显了放弃回答表现对QA任务准确性的意外影响，表明需要改进QA数据集的设计和评估方法，以更有效地评估模型放弃回答的正确性和下游影响。

科学问答中通过上下文扰动表征LLM回避行为