  Personalized dialogue systems aim to endow the chatbot agent with more
anthropomorphic traits for human-like interactions. Previous approaches have
explored explicitly user profile modeling using text descriptions, implicit
derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like
models. However, textual personas are limited in describing multi-faceted
attributes (\emph{e.g.}, \emph{language style, inner character nuances}),
implicit embedding suffers from personality sparsity, and handicraft prompts
lack fine-grained and stable controllability. Hence, these approaches may
struggle with complex personalized dialogue generation tasks that require
generating controllable responses with multiple personal attributes. To this
end, we propose \textbf{\textsc{Miracle}}, a novel personalized dialogue
generation method through \textbf{M}ult\textbf{I}ple Pe\textbf{R}sonal
\textbf{A}ttributes \textbf{C}ontrol within \textbf{L}atent-Space
\textbf{E}nergy-based Models. ttributes \textbf{C}ontrol within
\textbf{L}atent-Space \textbf{E}nergy-based Models. Specifically, our approach
first disentangles complex personality into multi-faceted attributes.
Subsequently, we employ a conditional variational auto-encoder to align with
the dense personalized responses within a latent joint attribute space. We have
also tailored a dedicated energy function and customized the ordinary
differential equations sampling method to offer flexible attribute composition
and precise attribute control. Extensive experiments demonstrate that
\textsc{Miracle} outperforms several strong baselines in terms of personality
controllability and response generation quality. Our dataset and code are
available at \url{https://github.com/LZY-the-boys/MIRACLE}
