Persona vectors: monitoring and controlling personality traits in language models

1. 01What problem does this paper attempt to solve?

   The authors state that large language models are prone to unexpected persona drift under prompts and fine-tuning, aiming to monitor and control trait changes through persona vectors in the representation space.

   Core question: Large language models frequently exhibit unexpected personality trait drift during deployment interaction and fine-tuning training (such as maliciousness, sycophancy, and hallucination). The paper aims to achieve automated monitoring, prediction and control of personality changes through linear directions (persona vectors) in the model's activation space.

   • Problem scenario and importance: Large language models are usually trained to be useful, harmless and honest "assistant" personas, but the authors note that model personas can easily drift during deployment due to system prompts or in-context demonstrations, for example producing threatening speech or inappropriate sycophancy; during fine-tuning, narrow-domain fine-tuning (such as generating unsafe code) can trigger cross-domain "emergent misalignment", and even routine reinforcement learning adjustments can unexpectedly induce excessive sycophancy.
   • Shortcomings of existing methods: The authors state that previous work on concept directions often required manually curated contrastive datasets for specific concepts, lacking a unified pipeline for automatically extracting high-level personality traits from natural language descriptions; during fine-tuning, there is a lack of effective tools for predicting model personality drift before fine-tuning occurs and for proactively preventing trait acquisition.
   • Core observation and hypothesis: Based on prior knowledge from representation engineering and concept activation intervention, the authors hypothesize that the model's high-level personality traits are encoded in linear directions in activation space; behavioral changes caused by fine-tuning are accompanied by displacement of activations along these linear directions; therefore, offsetting the optimization pressure in that direction in advance, or analyzing the projection of training data on the corresponding direction, can regulate or predict post-fine-tuning behavior.
   • The paper's main approach: The authors propose an automated persona vector extraction pipeline that requires only a trait name and short description to construct contrastive prompts and extract persona vectors in the residual stream; they then apply persona vectors to deployment-time activation monitoring, inference-time intervention, "preventive intervention" during fine-tuning, and pre-fine-tuning data projection difference filtering.

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2. 02What related research exists?

   The paper reviews three lines of work: linear representation of concepts, unexpected generalization in fine-tuning, and prediction and control of generalization behavior; the authors position this work as systematic extraction and full-process intervention.

   In the related work and introduction, the paper divides existing research into three categories and discusses their relevance to this work:

   • Linear representations of concepts and activation engineering

     • Concept directions and high-level trait regulation: Work by Turner et al. (2024), Zou et al. (2025) and Templeton et al. (2024) indicates that Transformer models represent interpretable concepts as linear directions in activation space; Panickssery et al. (2024), Arditi et al. (2024) and Ferrando et al. (2025) respectively showed that behaviors such as sycophancy, refusal and knowledge recognition are mediated by linear directions.
     • Contrastive mean-difference extraction and automated construction: Marks & Tegmark (2024) and Belrose (2023) used difference-in-means to extract concept directions; Wu et al. (2025) proposed an automated pipeline that uses large language models to synthesize contrastive data for extracting concept directions; the authors note that most other previous work required manually curated contrastive data for specific concepts.
     • Personality and emotion space analysis: Allbert et al. (2025) used system prompts to extract 179 personality trait vectors and analyzed their geometric space structure; Dong et al. (2025) extracted and applied emotion vectors for 5 basic emotions; Cunningham et al. (2023) and Bricken et al. (2023) decomposed activation space in an unsupervised manner through sparse autoencoders (SAE).

   • Unexpected generalization during fine-tuning

     • Emergent misalignment phenomenon: Betley et al. (2025) found that fine-tuning on narrow domains (such as vulnerable code) leads to broader misaligned behavior generalization; Dunefsky (2025), Soligo et al. (2025) and Wang et al. (2025) proposed that this generalization is mediated by meaningful linear directions in activation space.
     • Benign fine-tuning undermining safety: Qi et al. (2023) and He et al. (2024) noted that fine-tuning aligned models on benign data can weaken their safety guardrails; Gekhman et al. (2024) found that fine-tuning new knowledge increases the base model's tendency to hallucinate.

   • Prediction and control of generalized behavior

     • Representation-based data analysis and defense: He et al. (2024) used gradient and representation similarity to identify benign samples that reduce safety; Casademunt et al. (2025) proposed the CAFT method, which performs zero-ablation on specific concept directions during the forward pass of training; Yu et al. (2025) performed targeted ablation of refusal features during training; Zhou et al. (2024) used pre-trained detachable LoRA modules (safety vectors) activated during fine-tuning to block harmful parameter updates.

   • Comparison baselines and benchmark environments

     • Baseline methods: The authors compared CAFT, training-time loss regularization, negative system prompt mitigation, and preventative prompting in fine-tuning interventions.
     • Evaluation benchmarks: Experiments cover MMLU, HaluEval, MATH, GSM8K, GLOBALOPINIONQA, as well as the real interaction datasets LMSYS-CHAT-1M, TULU 3 SFT MIXTURE, ULTRA CHAT 200K and WILDCHAT.

   The authors position this paper as: establishing an end-to-end automated persona vector extraction pipeline, and extending the application of linear representations from deployment-time monitoring to proactive intervention during fine-tuning (preventive intervention) and pre-fine-tuning data projection difference filtering.

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3. 03How does the paper address this problem?

   The authors built an automated pipeline to extract residual-stream persona vectors, and proposed inference-time suppression intervention, fine-tuning-time preventive intervention, and pre-training data projection difference filtering.

   The authors propose an automated framework for extracting persona vectors based on contrastive prompts, and apply it to deployment monitoring, training-time intervention, and data filtering:

   Automated Persona Vector Extraction Pipeline

   • Generate trait-specific artifacts: Input trait name and short description, use Claude 3.7 Sonnet (temperature 1.0, thinking budget 2000 tokens) to generate three types of artifacts: 5 pairs of positive and negative system prompts (instructing the model to exhibit or suppress the trait respectively), 40 questions potentially eliciting the trait (20 for the extraction set, 20 for the evaluation set), and 1 evaluation prompt.
   • Construct contrastive responses and extract vectors: For the 20 questions in the extraction set, sample 10 responses each using positive and negative system prompts; score with GPT-4.1-mini according to the evaluation prompt (0–100), filtering valid responses where positive prompt scores are greater than 50 and negative prompt scores are less than 50; average residual stream activations over all tokens in responses at each layer; compute the mean activation difference vector between positive and negative responses.
   • Best layer selection: Test intervention effects of candidate vectors across layers, selecting the single-layer vector with the most significant trait expression change (for Qwen, layers 20 for evil and sycophancy, layer 16 for hallucination; for Llama, layer 16 for all three traits).

   Deployment-Time Activation Monitoring and Intervention

   • Causal directional intervention: During decoding, perturb residual stream activations along the extracted persona vector direction:

   h_ℓ ← h_ℓ + α · v_ℓ where α is a scalar intervention coefficient, v_ℓ is the persona vector extracted from layer ℓ, and h_ℓ is the residual stream activation at that layer. Negative coefficients (subtraction) suppress undesirable traits, positive coefficients (addition) enhance traits.

   • Last prompt token projection monitoring: Before generating responses, extract the hidden state of the last prompt token and project it onto the persona vector to predict the trait expression tendency of subsequent responses in advance.

   Fine-Tuning-Time Preventive Intervention

   • Preventive intervention mechanism: During the forward pass of fine-tuning, actively add the persona vector of undesirable traits to hidden states. The authors state the intuition is to place the model in the target undesirable trait state during training, thereby preemptively counteracting the optimization pressure that pushes model parameters toward that direction to fit the data.
   • All-layer incremental expansion: To eliminate cumulative effects across layers, the authors introduce layer-incremental vectors (difference between layer ℓ vector and layer ℓ−1 vector), applying fine-tuning-time perturbation across all layers to improve suppression and reduce capability loss.

   Pre-Fine-Tuning Data Projection Difference Filtering

   • Dataset-level projection difference: Given a training dataset D = {(x_i, y_i)}, compute the difference between the average projection of training responses onto the unit persona vector and the projection of the base model's natural responses to the same prompts:

   Δ P = 1/(|D|) ∑_i [a_ℓ(x_i, y_i) − a_ℓ(x_i, y′_i)] · v̂_ℓ where a_ℓ(x_i, y_i) is the average activation of training responses at layer ℓ, a_ℓ(x_i, y′_i) is the average activation of the base model's natural responses, and v̂_ℓ is the unit-normalized persona vector.

   • Sample-level filtering scheme: Sort by the projection difference of individual samples, and determine a filtering threshold based on the 95th percentile on a clean dataset (ULTRA CHAT 200K), removing samples above the threshold.

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4. 04What experiments did the paper conduct?

   The authors report correlation coefficients between persona vectors and trait expression of 0.76–0.97, and that preventive intervention suppresses persona drift while maintaining general capabilities.

   Experimental Setup

   • Models under test: Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct.
   • Auxiliary and evaluation models: Claude 3.7 Sonnet is used to generate prompts, evaluation questions, and synthetic fine-tuning responses; Claude 4.0 Sonnet is used to generate interpolated system prompts; GPT-4.1-mini serves as the judge model for scoring.
   • Experimental datasets: Constructed trait-inducing datasets for evil, sycophancy, and hallucination (Normal, Level I, Level II, each 5k–10k entries); constructed 5 types of emergent misalignment (EM-like) datasets (Medical 10k entries, Code 6k entries, MATH 7.5k entries, GSM8K 7.5k entries, GLOBALOPINIONQA 2.5k entries repeated 5 times); real chat datasets use LMSYS-CHAT-1M, TULU 3 SFT MIXTURE, ULTRA CHAT 200K, and WILDCHAT.
   • Metrics and evaluation methods: Trait expression score (TES, 0–100, extracted from the top 20 logits of GPT-4.1-mini output as a weighted sum of integer tokens 0–100); coherence score (0–100); general capability evaluation uses MMLU accuracy; correlation uses Pearson correlation coefficient r.
   • Fine-tuning hyperparameters: Single NVIDIA H100 GPU, rs-LoRA (rank=32, alpha=64, learning rate 1e-5, batch size=2, gradient accumulation 8 steps, fine-tuned for 1 epoch); real data filtering subset experiments use 500 samples fine-tuned for 10 epochs.
   • Human agreement check: Two authors blindly evaluated 300 pairs of responses (50 pairs per trait, comparing responses with scores greater than 80 and less than 20), with an overall agreement rate of 94.7% with the GPT-4.1-mini judge (Table 1).

   Main Results

   Swipe horizontally to read the table

   Model	Trait (Evil)	Trait (Sycophancy)	Trait (Hallucination)	Cross-trait baseline range
   Qwen2.5-7B-Instruct	r = 0.826 (p < 0.001)	r = 0.789 (p < 0.001)	r = 0.916 (p < 0.001)	r = 0.34–0.86 (Appendix G.2)
   Llama-3.1-8B-Instruct	r = 0.930 (p < 0.001)	r = 0.893 (p < 0.001)	r = 0.967 (p < 0.001)	r = 0.34–0.86 (Appendix G.2)

   (Note: Source from Figure 6 and Section 4.2; each cell value is the Pearson correlation coefficient between the projection of fine-tuning activation shift onto the persona vector and the model's post-fine-tuning trait expression score.)

   • Fine-tuning shift strongly correlates with trait scores: The authors state that the projection of fine-tuning-induced residual stream activation shift onto the corresponding persona vector is highly positively correlated with the model's post-fine-tuning trait expression score (r = 0.76–0.97), higher than the cross-trait baseline level (r = 0.34–0.86, Appendix G.2).
   • Unexpected persona drift is widespread: The authors point out that fine-tuning on datasets containing small errors in narrow domains (e.g., GSM8K, Math, Medical with incorrect solutions or unsafe code) leads to increased expression of non-target traits such as evil, sycophancy, or hallucination (Figure 5, Figure 16).
   • Cross-trait synergistic drift: The authors find that negative traits (and humor) tend to drift together during fine-tuning, and drift in the opposite direction to positive traits (optimism); the authors attribute this partly to the geometric correlations of the persona vectors themselves and data-level correlations (Appendix G.2).

   Preventive Intervention and Inference-Time Intervention Effects

   • What was tested: Compared the effects of inference-time vector suppression and fine-tuning-time preventive intervention on eliminating personality traits, and their impact on general capabilities (MMLU accuracy and coherence) (Figure 7, Figure 26).
   • Experimental results: Both types of intervention can suppress trait scores to the base model level (base model initial scores: evil 0, sycophancy 4.4, hallucination 20.1, per Figure 7). Under large intervention coefficients, inference-time intervention shows a decrease in MMLU accuracy, while preventive intervention maintains stable MMLU accuracy in the all-layer setting, and coherence scores remain above 80 (Figure 7, Figure 26). In the fact acquisition task (Figure 30), a fine-tuning-time preventive intervention coefficient of 1.25 suppresses hallucination to baseline level, with only a slight decrease in new fact accuracy.
   • Authors' interpretation: The authors believe that applying forward intervention in the corresponding direction during fine-tuning alleviates the optimization pressure to update parameters toward undesirable persona directions in advance, thus better balancing general capabilities than inference-time intervention.

   Pre-Training Data Filtering and Real Chat Data Validation

   • What was tested: On four real chat datasets—LMSYS-CHAT-1M, TULU 3 SFT MIXTURE, ULTRA CHAT 200K, WILDCHAT—fine-tuned models using top 500 (high projection difference), bottom 500 (low projection difference), and 500 random samples based on projection difference, and compared trait-inducing effects before and after explicit trait filtering by GPT-4.1-mini (filtering samples with scores greater than 1) (Figure 10, Figure 34).
   • Experimental results: Trait scores after fine-tuning on high projection difference samples are higher than those from random and low projection difference samples; after GPT-4.1-mini filtering of explicitly harmful samples, high projection difference samples still induce higher trait expression (Figure 10, Figure 34). In LMSYS-CHAT-1M, filtered high projection difference samples still uncover hallucination data caused by underspecified queries and covert role-playing sycophancy data (Table 7).
   • Authors' interpretation: The authors believe that persona vector projection difference filtering and LLM-judge-based filtering mechanisms have complementary advantages, capable of discovering seemingly harmless samples that drive model persona drift in internal representations.

   Sparse Autoencoder (SAE) Feature Decomposition

   • What was tested: Used BatchTopK SAE (k=64, expansion factor approximately 37x) to decompose persona vectors at layer 20 (evil, sycophancy) and layer 16 (hallucination) of Qwen-2.5-7B-Chat, and analyzed top-50 aligned features' max-activating samples and causal intervention effects on 500,000 pretraining and chat data entries (Figure 35, Table 8–10).
   • Experimental results: Top-5 features under intervention coefficients 1.5–2.5 all elicit trait expression scores of 78.6–98.6 (Table 8–10); the evil vector decomposes into specific features such as insults and abuse (Feature 12061, cosine similarity 0.336, intervention score 91.2), cruelty and sadism (Feature 128289, cosine similarity 0.306, score 84.9), and jailbreak attacks (Feature 71418, cosine similarity 0.257, score 84.8).
   • Authors' interpretation: The authors believe that persona vectors are composed of coarse-grained multi-faceted features, and SAE decomposition helps understand the fine-grained mechanisms behind persona vectors.

   Other Ablations and Analyses

   • Token position extraction ablation: Response avg outperforms Prompt avg and Prompt last in intervention effects across all three traits (Figure 11).
   • Projection difference vs. raw projection prediction comparison: On Qwen, predicting post-fine-tuning trait scores, projection difference correlation coefficients (evil: 0.946, sycophancy: 0.879, hallucination: 0.616) are higher than raw projection (evil: 0.784, sycophancy: 0.540, hallucination: 0.635) (Figure 8, Figure 21).
   • Prompt token approximate projection difference ablation: Using the last prompt token to approximate projection difference maintains predictive correlation on Qwen (evil: r = 0.931, sycophancy: r = 0.581, hallucination: r = 0.689) (Figure 23).
   • Single-layer vs. all-layer intervention ablation: All-layer incremental intervention achieves baseline suppression at smaller coefficients than single-layer intervention, and better maintains MMLU accuracy (Figure 26).
   • Comparison with CAFT concept ablation: CAFT is effective for evil and sycophancy but ineffective for hallucination (base model projection near 0 in that direction), while preventive intervention is effective on all three (Figure 27).
   • Training-time regularization loss ablation: Applying regularization penalty on activation projection in the persona direction still allows the model to express target traits after fine-tuning, failing to effectively suppress (Figure 28).

   Negative Results and Exceptions

   • In the fact acquisition task (Figure 30), inference-time intervention reduces model coherence and new fact memory accuracy.
   • CAFT cannot effectively suppress hallucination traits where the base model's initial projection is near 0 (Figure 27).
   • Training-time projection regularization cannot prevent the model from continuing to learn personality traits through alternative directions in the representation space (Figure 28).
   • When fine-tuning on extreme high projection difference samples from real data, the model exhibits non-target abnormal behaviors such as degraded generation quality and falling into story-telling mode (Section L.2).

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5. 05What are some points that can be further explored?

   The authors note that persona vector extraction is limited by supervised descriptions and the model's prompt induction assumption, and that single-turn evaluation and full-data generation incur environmental and computational overhead limitations.

   Limitations and Future Directions Noted by the Authors

   • Supervised and pre-specified trait dependence: The authors note that the pipeline is a supervised method requiring pre-specified target traits and precise natural language descriptions, unable to capture drift in unspecified traits; vague descriptions may cause extracted directions to deviate from intent (Section 8).
   • Applicability boundary of the system prompt induction assumption: The authors note that the pipeline relies on target traits being successfully inducible via system prompts in the model; although the tested Qwen and Llama have low refusal rates, this may not hold for models with stronger safety mechanisms that refuse to role-play malicious content (Section 8).
   • Evaluation reliance on automated judges and single-turn question settings: The authors note limitations of relying on GPT-4.1-mini scoring; each trait is evaluated with only 20 automatically generated questions in a single-turn setting, which cannot fully reflect trait performance in real deployment across domains, long contexts, and multi-turn interactions (Section 8).
   • Model scale and trait coverage: Experiments only cover two medium-sized models (Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct); the main text tests only 3 traits, with 4 additional traits tested in the appendix (Section 8).
   • Computational cost of full-data projection difference: Computing sample-level projection difference requires the base model to generate complete responses for each sample's prompt and perform two forward passes, which is computationally expensive on large-scale datasets; lower-cost approximation methods need exploration (Section 8, Section L.2).
   • Theoretical characterization of generalization mechanisms and the full persona space: In the conclusion, the authors note that the underlying mechanisms by which persona vectors generalize to causal intervention and prediction of fine-tuning behavior remain unclear; future work could explore the dimensional structure of the full persona space, whether a natural persona basis exists, and whether persona vector correlations predict trait co-expression (Section 9).

   Experimental Coverage

   • Model scope: Experiments cover two open-source dialogue models, Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct, excluding larger parameter models or closed-source commercial models.
   • Trait scope: The main text analyzes 3 negative traits—evil, sycophancy, and hallucination—while the appendix extends testing to 4 traits: optimistic, impolite, apathetic, and humorous.
   • Fine-tuning data construction scope: Fine-tuning experiments use 3 sets of trait-inducing datasets (each 5k–10k entries) and 5 types of domain-specific defect datasets (Medical 10k entries, Code 6k entries, MATH 7.5k entries, GSM8K 7.5k entries, Opinions 2.5k entries repeated 5 times), as well as 4 real chat datasets (LMSYS-CHAT-1M, TULU 3 SFT MIXTURE, ULTRA CHAT 200K, WILDCHAT, each randomly sampled up to 200,000 entries and preprocessed before filtering).
   • Fine-tuning training settings: Single NVIDIA H100 GPU, rs-LoRA (rank 32, alpha 64, lr 1e-5) for 1 epoch fine-tuning; real data filtering subset experiments use 500 samples trained for 10 epochs.
   • Evaluation and sampling specifications: Each trait evaluation set contains 20 single-turn questions; system prompt and few-shot experiments generate 10 responses per configuration; human agreement evaluation samples 300 pairs judged by 2 authors; the paper does not report variance across multiple fine-tuning runs, nor does it report multilingual task testing.

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6. 06Summarize the main content of the paper

   The authors establish an automated persona vector pipeline covering deployment monitoring, fine-tuning preventive intervention, and pre-training data filtering, providing a solution for understanding and controlling model persona drift.

   1. Research positioning and core question: The paper focuses on unexpected personality trait drift in large language models during deployment and fine-tuning (such as malicious speech, excessive sycophancy and confabulated information), exploring the feasibility of systematic monitoring, prevention and data filtering through linear representations in activation space.
   2. Automated extraction and multi-stage approach: The authors designed an automated extraction pipeline requiring only natural language trait descriptions, using contrastive prompts to extract persona vectors in the residual stream; they extended this to projection monitoring of the last prompt token during deployment, "preventive intervention" through additive perturbation in undesirable directions during fine-tuning, and pre-fine-tuning data filtering based on a "projection difference" metric.
   3. Fine-tuning drift strongly correlates with representation shift: In multi-dataset fine-tuning experiments on Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct, the Pearson correlation coefficient between the displacement of fine-tuning activations on persona vectors and post-fine-tuning trait scores reached 0.76–0.97 (Figure 6), which the authors said indicates that linear directions play a key mediating role in fine-tuning personality changes.
   4. Preventive intervention suppresses trait acquisition: Compared with inference-time subtractive intervention, which may harm the model's general capabilities, full-layer preventive intervention during fine-tuning can suppress undesirable trait expression to baseline levels (evil at 0, sycophancy at 4.4, hallucination at 20.1), while keeping MMLU accuracy stable and response coherence scores above 80 (Figure 7).
   5. Pre-fine-tuning projection difference can effectively filter hidden harmful data: In 4 real chat datasets including LMSYS-CHAT-1M, the top 500 samples selected by projection difference induced higher trait scores after fine-tuning than random samples; even after first filtering out explicitly harmful samples with GPT-4.1-mini, high-projection-difference samples still triggered personality drift (Figure 10), and the authors noted that this method has complementary advantages over large-model judges.
   6. The authors' implications and outlook: The authors believe personality traits are latent variables in the model that persist across tokens, and future work could further deconstruct fine-grained features through sparse autoencoders, and explore the basis structure of the full personality space and trait co-expression mechanisms.

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