LoRA-generating hypernetworks for efficient on-device LLM generative personalization
A hypernetwork generates user-specific LoRA adapters on device for personalized long-form LLM generation.
The paper trains a hypernetwork that maps a user's context tokens to a low-rank adaptation suited to that user. After shared artifacts are deployed, each device synthesizes its own LoRA with forward passes only, combining the feasibility of in-context learning with weight-level customization from parameter-efficient fine-tuning. The method reuses the target LLM's weights to limit extra storage and is evaluated on long-form text generation personalization against ICL and PEFT baselines.
- A hypernetwork maps user context tokens to a personalized LoRA.
- On-device personalization needs only forward passes, not full fine-tuning.
- Generated LoRA weights avoid extra latency from longer in-context prompts.
- The design reuses target LLM weights to limit added storage.
- Experiments target harder long-form generation personalization tasks.
From the original article
1 min read at arxiv.orgOn-device large language models (`LLMs'), e. g. running on mobile phones, are ripe for improvement via personalization.
By Sean Augenstein · arxiv.org
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