通过调整自我参照查询进行语言模型的人格编辑
Personality Editing for Language Models through Adjusting Self-Referential Queries
AI总结：
PALETTE通过调整自我参照查询实现语言模型的人格编辑，仅需12个样本即可显著提升人格对齐性。
摘要：
大型语言模型（LLMs）在诸如对话代理和内容创作等应用中至关重要，其中对模型人格的精确控制对于保持语气、一致性和用户参与度至关重要。然而，现有的基于提示或微调的方法要么缺乏鲁棒性，要么需要大规模训练数据，使其成本高昂且不切实际。在本文中，我们提出了PALETTE（通过LLM自我靶向查询进行人格调整），一种新的LLM人格编辑方法。我们的方法引入了调整查询，其中基于心理学构念的自我参照陈述被类比为事实性知识，从而能够直接编辑与人格相关的行为。与微调不同，PALETTE仅需12个编辑样本即可在人格维度上实现显著的改进。实验结果来自自动和人工评估，表明我们的方法使LLM的人格控制更加稳定和平衡。
英文摘要：
Large Language Models (LLMs) are integral to applications such as conversational agents and content creation, where precise control over a model's personality is essential for maintaining tone, consistency, and user engagement. However, prevailing prompt-based or fine-tuning approaches either lack robustness or demand large-scale training data, making them costly and impractical. In this paper, we present PALETTE (Personality Adjustment by LLM SElf-TargeTed quEries), a novel method for personality editing in LLMs. Our approach introduces adjustment queries, where self-referential statements grounded in psychological constructs are treated analogously to factual knowledge, enabling direct editing of personality-related responses. Unlike fine-tuning, PALETTE requires only 12 editing samples to achieve substantial improvements in personality alignment across personality dimensions. Experimental results from both automatic and human evaluations demonstrate that our method enables more stable and well-balanced personality control in LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
