Despite the remarkable advance of large language models (LLMs), the
prevalence of non-factual responses remains a common issue. This work studies
non-factuality prediction (NFP), which predicts whether an LLM will generate
non-factual responses to a question before the generation process. Previous
efforts on NFP usually rely on extensive computation. In this work, we conduct
extensive analysis to explore the capabilities of using a lightweight probe to
elicit ``whether an LLM knows'' from the hidden representations of questions.
Additionally, we discover that the non-factuality probe employs similar
patterns for NFP across multiple LLMs. Motivated by the intriguing finding, we
conduct effective transfer learning for cross-LLM NFP and propose a
question-aligned strategy to ensure the efficacy of mini-batch based training.

本研究通过使用轻量级探针从问题的隐藏表示中唤起 “语言模型是否知道” 的能力，对非真实反应预测（NFP）进行了广泛分析，发现非真实性探针在多个语言模型中采用类似模式，并提出了跨语言模型的有效迁移学习并使用问题对齐策略进行小批量训练以确保其效力。