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 (this https URL) is publicly available
for further exploration.

SPT 模型能够通过选择性提示调整语言模型以实现个性化对话，大幅提高响应的多样性，并改善其他关键性能指标，从而在促进吸引人和个性化对话生成方面显示出高效性。

个性化对话的选择性提示调整与 LLMs

Selective Prompting Tuning for Personalized Conversations with LLMs

Prompt tuning prepends a soft prompt to the input embeddings or hidden states
and only optimizes the prompt to adapt pretrained models (PTMs) to downstream
tasks. The previous work manually selects prompt layers which are far from
optimal and failed to exploit the potential of prompt tuning. In this work, we
propose a novel framework, \underline{S}elective \underline{P}rompt
\underline{T}uning (SPT), that learns to select the proper prompt layers by
inserting a prompt controlled by a learnable probabilistic gate at each
intermediate layer. We further propose a novel bi-level optimization framework,
SPT-DARTS, that can better optimize the learnable gates and improve the final
prompt tuning performances of the learned prompt layer settings. We conduct
extensive experiments with ten benchmark datasets under the full-data and
few-shot scenarios. The results demonstrate that our SPT framework can perform
better than the previous state-of-the-art PETuning baselines with comparable or
fewer tunable parameters.

本文提出了一种新颖的框架，Selective Prompt Tuning (SPT)，通过在每个中间层插入由可学习的概率门控制的提示来学习选择适当的提示层，进一步提出了一种新颖的双层优化框架 SPT-DARTS，可以更好地优化可学习门并改善所学提示层设置的最终提示调整性能，实验证明我们的 SPT 框架在全数据和少样本情况下比以往的最先进的 PETuning 基准方法表现更好，参数可调性相当或更少。