VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment
Aligning Large Language Models (LLMs) with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from Human Feedback (RLHF) often handle only coarse-grained attributes. In practice, fine-tuning LLMs on task-specific datasets to optimize value alignment inevitably incurs an alignment tax: the model's pre-calibrated value system drifts significantly due to latent bias absorption from training data, while the fine-tuning process also causes severe hallucinations and semantic information loss in generated responses. To address this, we propose VISA (Value Injection via Shielded Adaptation), a closed-loop framework designed to navigate this trade-of...
| Authors | Jiawei Chen, Tianzhuo Yang, Guoxi Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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