稀疏个性化文本生成与多轨迹推理
Sparse Personalized Text Generation with Multi-Trajectory Reasoning
AI总结：
本文提出PAT框架，通过双轨迹检索和强化学习机制提升冷启动场景下个性化文本生成的质量和对齐度。
摘要：
随着大型语言模型（LLMs）的发展，个性化已成为定制输出以满足个体用户需求的关键机制。然而，大多数现有方法依赖密集的交互历史，使其在冷启动场景中（数据稀疏或不可用）效果不佳。尽管外部信号（如相似用户的内容）可能提供解决方案，但有效利用这些信号仍具挑战性：原始上下文常含噪声，现有方法难以处理异构数据源。为解决这些问题，我们引入PAT（基于对齐轨迹的个性化），一种用于冷启动LLM个性化推理框架。PAT首先沿两个互补轨迹检索信息：从风格相似的用户获取写作风格提示，从偏好匹配的用户获取主题特定上下文。然后，它采用基于强化学习的迭代双推理机制，使LLM能够联合优化和整合这些信号。在真实世界个性化基准测试中，PAT在稀疏数据条件下持续提升生成质量和对齐度，建立了冷启动个性化问题的有力解决方案。
英文摘要：
As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on dense interaction histories, making them ineffective in cold-start scenarios where such data is sparse or unavailable. While external signals (e.g., content of similar users) can offer a potential remedy, leveraging them effectively remains challenging: raw context is often noisy, and existing methods struggle to reason over heterogeneous data sources. To address these issues, we introduce PAT (Personalization with Aligned Trajectories), a reasoning framework for cold-start LLM personalization. PAT first retrieves information along two complementary trajectories: writing-style cues from stylistically similar users and topic-specific context from preference-aligned users. It then employs a reinforcement learning-based, iterative dual-reasoning mechanism that enables the LLM to jointly refine and integrate these signals. Experimental results across real-world personalization benchmarks show that PAT consistently improves generation quality and alignment under sparse-data conditions, establishing a strong solution to the cold-start personalization problem.
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。
