让大模型持续学习用户习惯，实现长期个性化服务。
AI PERSONA: Towards Life-long Personalization of LLMs
- 提出长期个性化框架，让模型随用户变化动态调整。
- 设计真实场景下的合成数据与评估指标，支持可靠测试。
- 开源代码数据，推动个性化大模型研究发展。
本文提出大语言模型的长期个性化任务。当前主流研究聚焦于通过扩大数据和算力提升模型能力，但我们认为，让语言模型系统或语言智能体持续适应每个用户的多样化且不断变化的特征，并提供实时个性化的帮助同样至关重要。为此，我们给出了清晰的任务定义，并提出一个简单、通用、高效且可扩展的长期个性化框架。为促进未来相关研究，我们还引入了生成真实基准数据的方法和鲁棒的评估指标。所有代码与数据将公开，用于构建和评测长期个性化的大语言模型系统。
原文摘要 · Abstract (English)
In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language agents, to continuously adapt to the diverse and ever-changing profiles of every distinct user and provide up-to-date personalized assistance. We provide a clear task formulation and introduce a simple, general, effective, and scalable framework for life-long personalization of LLM systems and language agents. To facilitate future research on LLM personalization, we also introduce methods to synthesize realistic benchmarks and robust evaluation metrics. We will release all codes and data for building and benchmarking life-long personalized LLM systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。