  In conversational AI, personalizing dialogues with persona profiles and
contextual understanding is essential. Despite large language models' (LLMs)
improved response coherence, effective persona integration remains a challenge.
In this work, we first study two common approaches for personalizing LLMs:
textual prompting and direct fine-tuning. We observed that textual prompting
often struggles to yield responses that are similar to the ground truths in
datasets, while direct fine-tuning tends to produce repetitive or overly
generic replies. To alleviate those issues, we propose \textbf{S}elective
\textbf{P}rompt \textbf{T}uning (SPT), which softly prompts LLMs for
personalized conversations in a selective way. Concretely, SPT initializes a
set of soft prompts and uses a trainable dense retriever to adaptively select
suitable soft prompts for LLMs according to different input contexts, where the
prompt retriever is dynamically updated through feedback from the LLMs.
Additionally, we propose context-prompt contrastive learning and prompt fusion
learning to encourage the SPT to enhance the diversity of personalized
conversations. Experiments on the CONVAI2 dataset demonstrate that SPT
significantly enhances response diversity by up to 90\%, along with
improvements in other critical performance indicators. Those results highlight
the efficacy of SPT in fostering engaging and personalized dialogue generation.
The SPT model code (https://github.com/hqsiswiliam/SPT) is publicly available
for further exploration.
