Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning
Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We investigate a related but distinct failure mode: warmth fine-tuning also weakens adversarial safety, making models more susceptible to jailbreaks and harmful output generation. We examine whether this reflects an inherent consequence of empathetic adaptation or an artifact of data construction. To address this, we introduce a persona-driven rewriting pip
Record details
Published: 26 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (26 June 2026), “Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning,” evidence record 538, https://ethics.ai/record/538 (originally published by arXiv).
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