Academic

VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment

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 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 (GRPO) with a composite reward function that simultaneously o

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Jiawei Chen, Tianzhuo Yang, Guoxi Zhang, Jiaming Ji, Yaodong Yang, Juntao Dai · · 1 min read · 2 views
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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 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 (GRPO) 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.

Executive Summary

This article proposes VISA (Value Injection via Shielded Adaptation), a novel framework designed to align Large Language Models (LLMs) with nuanced human values while minimizing the alignment tax, which occurs when a model's pre-calibrated value system drifts due to latent bias absorption from training data. VISA incorporates a high-precision value detector, a semantic-to-value translator, and a core value-rewriter, which are trained via Group Relative Policy Optimization (GRPO) with a composite reward function. The framework effectively mitigates the alignment tax while preserving semantic integrity, outperforming standard fine-tuning methods and prompting-based baselines. The authors' experiments demonstrate the efficacy of VISA in maintaining factual consistency and general capabilities. This study contributes significantly to the development of value-aligned LLMs and sheds light on the trade-offs between value alignment and semantic integrity.

Key Points

  • ▸ VISA framework addresses the alignment tax in LLMs by injecting nuanced human values through shielded adaptation
  • ▸ The framework incorporates a high-precision value detector, a semantic-to-value translator, and a core value-rewriter
  • ▸ GRPO with a composite reward function optimizes for fine-grained value precision and semantic integrity

Merits

Strength in addressing the alignment tax

VISA effectively minimizes the alignment tax by learning an optimal policy to balance competing objectives, preserving semantic integrity and factual consistency.

Demerits

Limited generalizability

The framework's performance may not generalize to diverse value alignment tasks and domains.

Expert Commentary

The proposed VISA framework presents a promising approach to addressing the alignment tax in LLMs. However, the evaluation of VISA's performance should be extended to diverse value alignment tasks and domains to ensure its generalizability. Furthermore, the study's findings have significant implications for policymakers and industry stakeholders, as they underscore the importance of developing value-aligned LLMs for responsible AI practices. To further advance this research, future studies should investigate the applicability of VISA to broader AI applications and explore its potential integration with other value alignment techniques.

Recommendations

  • ✓ Future research should investigate the applicability of VISA to diverse value alignment tasks and domains
  • ✓ The VISA framework should be integrated with other value alignment techniques to enhance its performance and generalizability

Sources

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