面向个性化对话的选择性提示调优
Selective Prompting Tuning for Personalized Conversations with LLMs
AI总结：
针对LLMs个性化对话中文本提示效果不佳和直接微调易产生重复回复的问题，提出选择性提示调优（SPT），通过可训练密集检索器自适应选择软提示，结合对比和融合学习，在CONVAI2上使回复多样性提升高达90%。
摘要：
在对话式人工智能中，利用人物档案和上下文理解来个性化对话至关重要。尽管大型语言模型（LLMs）提高了回复的连贯性，但有效的人物整合仍然是一个挑战。在这项工作中，我们首先研究了两种常见的个性化LLMs的方法：文本提示和直接微调。我们观察到，文本提示往往难以产生与数据集中真实答案相似的回复，而直接微调则倾向于产生重复或过于通用的回复。为了缓解这些问题，我们提出了选择性提示调优（SPT），它以选择性的方式对LLMs进行软提示，以实现个性化对话。具体来说，SPT初始化一组软提示，并使用一个可训练的密集检索器，根据不同的输入上下文自适应地为LLMs选择合适的软提示，其中提示检索器通过LLMs的反馈动态更新。此外，我们提出了上下文-提示对比学习和提示融合学习，以鼓励SPT增强个性化对话的多样性。在CONVAI2数据集上的实验表明，SPT显著提高了回复多样性，最高可达90%，同时在其他关键性能指标上也有所提升。这些结果凸显了SPT在促进引人入胜和个性化的对话生成方面的有效性。SPT模型代码（https://github.com/hqsiswiliam/SPT）已公开，可供进一步探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
