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Persona Vectors: Monitoring and Controlling Character Traits in Language Models

NeurIPS 2026 · Large language models › Instruction and fine-tuning · approach: Safety alignment after fine-tuning

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Extracts "persona vectors" from natural-language trait descriptions via contrastive system prompts and uses them to monitor, steer, and pre-screen training data for persona shifts (evil, sycophancy, hallucination) in Qwen2.5-7B and Llama-3.1-8B.

Problem

LLM personas (e.g., evil, sycophancy, hallucination) can shift unexpectedly at deployment via prompting and during finetuning, and there is no automated, general tool to monitor, control, or predict these shifts.

Approach

Automatically generate contrastive positive/negative system prompts and eval questions from a trait description, take the difference in mean response activations as a persona vector, and use it for steering, activation monitoring, preventative steering during finetuning, and projection-difference data screening.

Key results

• Finetuning shift along persona vectors correlates with post-finetuning trait expression at r = 0.76–0.97.
• Preventative steering reduces training-induced persona shifts while preserving MMLU better than inference-time steering.
• LLM judge agreed with human judges on 94.7% of 300 pairwise comparisons.

Surprising findings

• Training on flawed math reasoning increases expression of evil, despite no explicit evil content in the data.
• Persona vector-based filtering surfaces 'underspecified query' hallucination-inducing samples that evade LLM-based hallucination filters.

Limitations

• Supervised pipeline requires a precise trait description and that the trait is inducible via system prompting.
• Evaluation relies on a small set of auto-generated single-turn questions and an LLM judge that is imperfect.
• Experiments limited to two mid-size chat models and three main traits.

Experimental setup

Datasets: LMSYS-Chat-1M UltraChat 200k HaluEval MMLU custom evil/sycophancy/hallucination finetuning datasets EM-like medical/code/math/opinion datasets

Benchmarks: MMLU HaluEval LMSYS-Chat-1M-based finetuning evaluation

Metrics: trait expression score Pearson correlation coherence score MMLU accuracy agreement rate with human judges

Baselines: CAFT inference-time steering system prompting many-shot prompting LLM judge data filtering

Builds on: emergent misalignment difference-in-means steering CAFT contrastive activation addition

Closest papers by meaning

• Who's asking? User personas and the mechanics of latent misalignment (NeurIPS 2024 · similarity 0.88)
• Persona-Model Collapse in Emergent Misalignment (NeurIPS 2026 · similarity 0.88)
• Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs (ICLR 2024 · similarity 0.86)
• PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning (ICML 2024 · similarity 0.86)
• Personality Alignment of Large Language Models (ICLR 2025 · similarity 0.86)
• PersonaManifold: Revealing and Exploiting Curved Geometry in LLM Persona Representations (NeurIPS 2026 · similarity 0.86)