  Large Language Models (LLMs), such as \texttt{ChatGPT}, greatly empower
dialogue systems with strong language understanding and generation
capabilities. However, most of the previous works prompt the LLMs to directly
generate a response based on the dialogue context, overlooking the underlying
linguistic cues about the user status exhibited in the context. Such in-depth
dialogue scenarios are challenging for existing LLMs to figure out the user's
hidden needs and respond satisfactorily through a single-step inference. To
this end, we propose a novel linguistic cue-based chain-of-thoughts
(\textit{Cue}-CoT), which enhances the LLMs inference with an intermediate
reasoning step to find cues exhibited in the dialogue, aiming to provide a more
personalized and engaging response. To evaluate the approach, we build a
benchmark with in-depth dialogue questions, consisting of 6 datasets in both
Chinese and English, targeting 3 major linguistic cues during the conversation:
\textit{personality}, \textit{emotion}, and \textit{psychology}. We conduct
extensive experiments on the proposed benchmark with 5 LLMs under both
zero-shot and one-shot settings. Empirical results demonstrate our proposed
\textit{Cue}-CoT method outperforms standard prompting methods in terms of both
\textit{helpfulness} and \textit{acceptability} on all datasets.
