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VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment
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VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment

#VISA #value injection #shielded adaptation #personalized LLM #AI alignment #large language models #ethical AI

📌 Key Takeaways

  • VISA is a new method for aligning large language models (LLMs) with personal user values.
  • It uses a 'shielded adaptation' technique to inject values while maintaining model safety.
  • The approach aims to personalize LLM outputs without compromising core ethical guidelines.
  • This research addresses the challenge of customizing AI behavior for individual preferences.

📖 Full Retelling

arXiv:2603.04822v1 Announce Type: new Abstract: 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

🏷️ Themes

AI Alignment, Personalization

📚 Related People & Topics

Visa Inc.

Visa Inc.

American payment card services corporation

Visa Inc. () is an American multinational payment card services corporation headquartered in San Francisco, California. It facilitates electronic funds transfers throughout the world, most commonly through Visa-branded credit cards, debit cards and prepaid cards.

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AI alignment

Conformance of AI to intended objectives

In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.

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Mentioned Entities

Visa Inc.

Visa Inc.

American payment card services corporation

AI alignment

Conformance of AI to intended objectives
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2603.04822 [Submitted on 5 Mar 2026] Title: VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment Authors: Jiawei Chen , Tianzhuo Yang , Guoxi Zhang , Jiaming Ji , Yaodong Yang , Juntao Dai View a PDF of the paper titled VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment, by Jiawei Chen and 5 other authors View PDF HTML Abstract: Aligning Large Language Models with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from Human Feedback 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-off. VISA's architecture features a high-precision value detector, a semantic-to-value translator, and a core value-rewriter. The value-rewriter is trained via Group Relative Policy Optimization with a composite reward function that simultaneously optimizes for fine-grained value precision, and the preservation of semantic integrity. By learning an optimal policy to balance these competing objectives, VISA effectively mitigates the alignment tax while staying loyal to the original knowledge. Our experiments demonstrate that this approach enables precise control over a model's value expression while maintaining its factual consistency and general capabilities, significantly outperforming both standard fine-tuning methods and prompting-based baselines, including GPT-4o. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04822 [cs.AI] (or arXiv:2603.04822v1 [cs.AI]...
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Source

arxiv.org

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