知识图谱微调：基于人类反馈的实时大型语言模型个性化
Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback
AI总结：
针对LLM实时个性化中反向传播成本高且可解释性低的问题，提出KGT方法，通过提取用户反馈中的知识三元组优化知识图谱而不修改模型参数，显著提升个性化性能并降低延迟与内存开销。
摘要：
大型语言模型（LLMs）在一系列自然语言处理任务中展现出了卓越的能力。一旦部署，LLMs会遇到具有个性化事实知识的用户，这种个性化知识通过用户与LLMs的交互持续反映出来。为了提升用户体验，实时模型个性化至关重要，它允许LLMs在人机交互期间根据用户反馈适应用户特定知识。现有方法大多需要反向传播来微调模型参数，这带来了高昂的计算和内存开销。此外，这些方法的可解释性较低，在长期使用中会对模型性能造成不可预见的影响，因为此时用户的个性化知识已大量积累。为了应对这些挑战，我们提出了知识图谱微调（KGT），这是一种利用知识图谱（KGs）来个性化LLMs的新方法。KGT从用户的查询和反馈中提取个性化事实知识三元组，并在不修改LLM参数的情况下优化KGs。我们的方法通过避免反向传播提高了计算和内存效率，并通过使KG的调整对人类可理解来确保可解释性。在包括GPT-2、Llama2和Llama3在内的最先进LLMs上进行的实验表明，KGT显著提升了个性化性能，同时降低了延迟和GPU内存开销。最终，KGT为用户与LLMs交互期间有效、高效且可解释的实时LLM个性化提供了一个有前景的解决方案。
英文摘要：
Large language models (LLMs) have demonstrated remarkable proficiency in a range of natural language processing tasks. Once deployed, LLMs encounter users with personalized factual knowledge, and such personalized knowledge is consistently reflected through users' interactions with the LLMs. To enhance user experience, real-time model personalization is essential, allowing LLMs to adapt user-specific knowledge based on user feedback during human-LLM interactions. Existing methods mostly require back-propagation to finetune the model parameters, which incurs high computational and memory costs. In addition, these methods suffer from low interpretability, which will cause unforeseen impacts on model performance during long-term use, where the user's personalized knowledge is accumulated extensively.To address these challenges, we propose Knowledge Graph Tuning (KGT), a novel approach that leverages knowledge graphs (KGs) to personalize LLMs. KGT extracts personalized factual knowledge triples from users' queries and feedback and optimizes KGs without modifying the LLM parameters. Our method improves computational and memory efficiency by avoiding back-propagation and ensures interpretability by making the KG adjustments comprehensible to humans.Experiments with state-of-the-art LLMs, including GPT-2, Llama2, and Llama3, show that KGT significantly improves personalization performance while reducing latency and GPU memory costs. Ultimately, KGT offers a promising solution of effective, efficient, and interpretable real-time LLM personalization during user interactions with the LLMs.
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。
