DPPM: Dual-Path Parametric Memory for Personalized Language Models
Long-term personalization requires language models to use interaction history to track users' preferences across sessions. Parametric memory encodes this interaction history into model parameters or adapters, reducing the need to include it in the inference context. However, independent context compilation leaves cross-session integration unspecified, while recurrent updates can attenuate earlier evidence. To address these challenges, we propose Dual-Path Parametric Memory (DPPM). Its Evidence path directly pools representations of the interaction history to preserve earlier evidence, while its Delta path sequentially updates an associative state to capture changes. Fusing both outputs pr...
| Authors | Yuhao Chen, Shuochen Liu, Jiayao Shi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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