Sparse Personalized Text Generation with Multi-Trajectory Reasoning
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 retrieve...
| Authors | Bo Ni, Haowei Fu, Qinwen Ge et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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