价值对齐的LLM反而更易生成有害内容，研究揭示其心理机制
Unintended Harms of Value-Aligned LLMs: Psychological and Empirical Insights
- 基于用户价值观微调模型，使其按价值生成文本
- 对齐模型在安全测试中表现更差，特定价值观与风险显著相关
- 提出上下文对齐方法提升安全性，适合安全评估与伦理设计者
大型语言模型（LLMs）的应用持续扩展，个性化且符合人类价值观的模型日益受到关注。然而，将模型与个体价值观对齐可能带来重大安全风险，因某些价值观可能关联有害信息。本文识别出价值对齐型LLM的安全隐患，并探究其背后的心理机制。研究发现：(1) 价值对齐模型相比非微调模型更易产生有害行为，在传统安全评估中风险略高于其他微调模型；(2) 安全问题源于模型真正依据对齐价值观生成内容，从而放大有害输出。基于包含详细安全类别的数据集，我们发现价值对齐与安全风险存在显著相关性，支持心理理论假设。本研究揭示了价值对齐的“黑箱”机制，并提出上下文对齐方法以增强价值对齐模型的安全性。
原文摘要 · Abstract (English)
The application scope of Large Language Models (LLMs) continues to expand, leading to increasing interest in personalized LLMs that align with human values. However, aligning these models with individual values raises significant safety concerns, as certain values may correlate with harmful information. In this paper, we identify specific safety risks associated with value-aligned LLMs and investigate the psychological principles behind these challenges. Our findings reveal two key insights. (1) Value-aligned LLMs are more prone to harmful behavior compared to non-fine-tuned models and exhibit slightly higher risks in traditional safety evaluations than other fine-tuned models. (2) These safety issues arise because value-aligned LLMs genuinely generate text according to the aligned values, which can amplify harmful outcomes. Using a dataset with detailed safety categories, we find significant correlations between value alignment and safety risks, supported by psychological hypotheses. This study offers insights into the "black box" of value alignment and proposes in-context alignment methods to enhance the safety of value-aligned LLMs.
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