The way and content in which users ask questions can provide insight into
their current status, including their personality, emotions, and psychology.
Instead of directly prompting the large language models (LLMs), we explore how
chain-of-thought prompting helps in this scenario to perform reasoning and
planning according to user status, aiming to provide a more personalized and
engaging experience for the user query. To this end, we first construct a
benchmark of 6 dialogue or question-answering datasets in both English and
Chinese, covering 3 different aspects of user status (\textit{including}
\textit{personality}, \textit{emotion}, and \textit{psychology}). Then we
prompt the LLMs to generate the response regarding the user status as
intermediate reasoning processing. We propose a novel demonstration selection
strategy using the semantic similarity of intermediate reasoning instead of
test queries. To evaluate the effectiveness and robustness of our approach, we
conduct extensive experiments with 7 LLMs under zero-shot and one-shot
settings. The experimental results show that our approach consistently
outperforms standard prompting in terms of both \textit{helpfulness} and
\textit{acceptness} across all datasets, regardless of the LLMs used. The code
and dataset can be found at
https://github.com/ruleGreen/Dialogue\_CoT.git.

本文通过构建 6 个涵盖用户状态的（包括人格、情绪和心理）中英数据集，探索了 CoT 方式来帮助大型语言模型进行推理和规划，以提供更个性化和优质的用户体验，并提出了一种新颖的演示选择策略，使用中间推理的语义相似性进行，实验证明了我们的方法相对标准提示在所有数据集上均表现出更好的有效性和稳健性。