Large language models (LLMs) have been shown to possess impressive
capabilities, while also raising crucial concerns about the faithfulness of
their responses. A primary issue arising in this context is the management of
unanswerable queries by LLMs, which often results in hallucinatory behavior,
due to overconfidence. In this paper, we explore the behavior of LLMs when
presented with unanswerable queries. We ask: do models \textbf{represent} the
fact that the question is unanswerable when generating a hallucinatory answer?
Our results show strong indications that such models encode the answerability
of an input query, with the representation of the first decoded token often
being a strong indicator. These findings shed new light on the spatial
organization within the latent representations of LLMs, unveiling previously
unexplored facets of these models. Moreover, they pave the way for the
development of improved decoding techniques with better adherence to factual
generation, particularly in scenarios where query unanswerability is a concern.

当生成虚幻答案时，大型语言模型表达问题是否无法回答的事实，结果表明，LLMs 的潜在表示中存在空间组织机制，揭示了这些模型以前未被探索的方面。同时，为了更好地遵守事实生成，特别是在问题无法回答的情况下，这些发现为改进解码技术的发展铺平了道路。