Scaling Personality Control in LLMs with Big Five Scaler Prompts

arXiv:2508.06149 · cs.CL, cs.MA · Submitted 2025-08-08 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Scaling Personality Control in LLMs with Big Five Scaler Prompts".

Jane: The gist The Big5-Scaler framework presents a prompt-based method for conditioning large language models with controllable Big Five personality traits without requiring additional training or curated character data.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Let's talk about the title and who wrote this paper, Scaling Personality Control in LLMs with Big Five Scaler Prompts. It’s pretty direct about what they are doing.

Jane: It’s essentially saying they developed a method to scale personality control in large language models using these specific Big Five Scaler prompts, which implies they've put a systematic way to do this into practice for testing across different models and tasks.

Lu: The authors are Gunhee Cho and Yun-Gyung Cheong from Sungkyunkwan University in South Korea, and the paper clearly lays out their contributions right away.

Meng: I read the abstract again, and it emphasizes that the main contribution is Big5-Scaler itself, a prompt-based framework that doesn't require any additional training or curated character data to condition LLMs with controllable Big Five personality traits.

Lalam: They also mention they evaluate this across trait expression, dialogue generation, and human trait imitation tasks to show how effective the method is in practice.

Tom: So it’s not just a theoretical concept; they’ve actually run tests on how well this works across different LLMs and what kind of results you can expect based on the prompt type and scale used.

Jane: It suggests that we can achieve personality control by embedding explicit numeric values into natural language prompts, which is a significant shift because it moves the control mechanism right at inference time.

Lu: It’s about making personality traits interpretable and controllable by mapping them directly to a set of numbers within the prompt structure.

Meng: I’m thinking about the practical side—if this works without finetuning, that drastically lowers the barrier for deploying models with specific personas for customer service or anything like that.

The paper's summary: Tom: So to summarize what they found in this paper, Big5-Scaler is a framework where you use Big Five theory to induce interpretable and controllable personality traits in LLMs by using these prompt-based methods.

Jane: They propose that we can introduce a trait-level control mechanism by assigning explicit numerical values to each of the five Big Five dimensions, which lets us modulate personality expression directly at inference time.

Lu: The framework involves constructing persona prompts using three types: simple, specific, and simspec prompts, and assigning agents based on their assigned Big Five trait values.

Meng: Then they detail how an agent gets a memory buffer initialized with its specific prompt, so that the agent maintains that personality conditioning across multi-turn dialogues.

Lalam: The agents then engage in turn-based dialogue where each new utterance is generated conditioned on its current memory, which allows them to maintain their distinct personality conditioning while responding to the shared dialogue history.

Tom: So it’s a system where the model’s personality isn't fixed but is dynamically shaped by the prompt structure and how that agent interacts with its own memory throughout the conversation.

Jane: And they conduct comprehensive empirical evaluations, showing that these generated outputs consistently reflect the specified trait configurations across different models and tasks.

The paper's improvements: Tom: One of the main things they highlight in their analysis is that using concise prompts and lower trait intensities tends to be more effective when you’re aiming for the lowest root mean squared error in trait alignment.

Jane: They found that scale ten consistently resulted in the lowest RMSE, which means smaller intensity levels are generally more reliably interpreted by the models than going straight to high values <ref:2508.06149#pg3>.

Lu: They also specifically point out that the best-performing configuration they tested involved using the Phi4-14B model combined with a simple prompt and a scale of ten.

Meng: That’s a really helpful piece of information for us when we’re thinking about deployment because it tells us exactly where to start testing for the most reliable personality control without needing massive compute budgets.

Lalam: And they noted that while all methods performed better at inducing target traits compared to baseline methods, Neuroticism was an exception where all tested methods underperformed due to safety alignment objectives in the models.

Tom: So while this is efficient for expressing traits like Openness or Conscientiousness, we still have those safety constraints kicking in when trying to dial up Neuroticism.

Jane: This suggests that the Big5-Scaler method is an efficient and effective prompting strategy for inducing personality-aligned behavior in LLMs, especially for those non-safety sensitive dimensions.

Conclusion: Tom: So, wrapping this up with the Scaling Personality Control in LLMs with Big Five Scaler Prompts paper, the main implication is that we can use prompt engineering to give us fine-grained control over personality traits without any extra training.

Jane: This means building agents that maintain consistent behavior across multi-turn interactions becomes much more feasible because we have a systematic way to condition them from the start.

Lu: The paper shows that the effectiveness depends heavily on choosing the right prompt type and intensity scale, with simpler prompts at lower scales proving most effective for achieving good alignment.

Meng: From a production standpoint, we can use these findings to design more efficient ways to deploy personality-aware agents because we know which combinations of model and prompt structure will give us the best results without needing huge fine-tuning efforts.

Lalam: I think the main point is that mapping human Big Five scores into Big5-Scaler prompts allows agents to generate responses that align with target personality profiles, and their imitation experiments showed RMSE values clustering around one point eight in those cases.

Tom: So we have a prompt-based method for inducing personality alignment, but we still have these caveats about model capability and safety constraints that need addressing in future work.

Jane: It’s a solid starting point for anyone looking to build models that exhibit more consistent and distinguishable personalities in dialogue research, and it points toward using simpler prompts at lower scales as the most efficient path right now.

Gunhee Cho, *Yun-Gyung Cheong

Sungkyunkwan University

cs.CL, cs.MA

Submitted: 2025-08-08

Updated: 2026-10-14

Code: https://github.com/tatsu-lab/stanford_alpaca

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 91/100

The gist: The gist The Big5-Scaler framework presents a prompt-based method for conditioning large language models with controllable Big Five personality traits without requiring additional training or curated

Key concepts

Big Five Personality Traits: These are five broad dimensions used to describe human personality: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The framework uses these specific traits as the targets for controlling an LLM's generated behavior.
Prompt Types: The method defines three prompt structures: Simple Prompt (high-level descriptions), Specific Prompt (facet-level behavioral descriptions), and Simspec Prompt (a combination of both). These types allow users to choose the level of detail when instructing the LLM on personality.
Trait Intensity Scale: This is a configurable numeric scale ranging from 10 to 100, representing the desired strength or degree of a specific trait. The framework tests different scales, finding that lower intensity levels (like 10) are often more reliably interpreted by the models.
Memory Buffer (Mi): Each agent maintains a memory buffer initialized with its specific personality prompt. This buffer is used during turn-based dialogue to condition each subsequent utterance, ensuring the agent responds consistently with its assigned personality throughout a conversation.

Terminology

Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes

Summary

The gist The Big5-Scaler framework presents a prompt-based method for conditioning large language models with controllable Big Five personality traits without requiring additional training or curated character data.

How it works

Big5-Scaler is a prompt-based framework that encodes trait intensity values directly into natural language prompts, enabling fine-grained control over the degree of trait expression at inference time We define three types of prompts: Simple Prompt, Specific Prompt, and Simspec Prompt The trait intensity scale n is configurable with four discrete levels: 10, 25, 50, and 100 The Simple Prompt consists of High-level descriptions of the five traits The Specific Prompt includes Facet-level behavioral descriptions for each trait The Simspec Prompt is a combination of both trait-level and facet-level descriptions Each agent is initialized with a personality prompt pi generated using Big5-Scaler, and it maintains a memory buffer Mi initialized with its personality prompt Agents engage in turn-based dialogue where each agent generates an utterance mj conditioned on its current memory This process continues iteratively, enabling agents to maintain distinct personality conditioning while responding to a shared dialogue history.

Empirical Evaluation

The framework is evaluated across several tasks, including trait expression, dialogue generation, and human trait imitation Models tested include LLaMA3-8B, Mistral-25B, and Phi4-14B The evaluation assesses whether the generated outputs clearly reflect the intended Big Five traits

** Single-Trait Expression Ability: This experiment evaluates the model’s ability to express a specific Big Five trait when explicitly instructed to maximize that trait The results show that models guided by Big5-Scaler exhibit stronger expression of target traits compared to baseline methods. However, the Neuroticism trait is an exception where all methods underperform due to safety alignment objectives. The results demonstrate that Big5-Scaler provides an efficient and effective prompting strategy for inducing personality-aligned behavior in LLMs. 5.1 Single-Trait Expression Ability > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism NAIVE 3.17 1.29 3.19 1.11 2.85 NAIVE Prompting. The results show that scale 10 consistently resulted in the lowest RMSE, indicating that smaller-scale intensity levels are more reliably interpreted. (a) LLaMA3-8b (b) Mistral-25b (c) Phi-4-14b > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 27.715 34.301 30.346 30.787 > Table 7. The best-performing configuration was using the Phi4-14B model, combined with the simple prompt and a scale of 10. (c) Phi-4-14b > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 17.786 23.991 22.983 21.587 > Table 7. (b) Mistral-25B > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 23.148 28.402 25.891 25.814 > Table 7. (a) LLaMA3-8B > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 30.331 33.347 33.032 32.237 > Table 7. (c) Phi-4-14B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 17.786 23.991 22.983 21.587 > Table 7. (b) Mistral-25B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 23.148 28.402 25.891 25.814 > Table 7. (a) LLaMA3-8B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 30.331 33.347 33.032 28, Table 7. (c) Phi-4-14B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 17.786 23.991 22.983 21.587 > Table 7. (b) Mistral-25B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 23.148 28.402 25.891 25.814 > Table 7. (a) LLaMA3-8B > 3, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 30.331 33.347 26.282 28, Table 7. (c) Phi-4-14B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 17.786 31.833 26.893 28, Table 7. (b) Mistral-25B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 23.148 25.177 38.857 32, Table 7. (a) LLaMA3-8B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 30.670 26.959 33.264 27, Table 7. (c) Phi-4-14B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 17.786 29.842 35.217 32, Table 7. (b) Mistral-25B > 4, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 23.148 30.936 32.314 28, Table 7. (a) LLaMA3-8B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 30.670 32.600 31.679 28, Table 7. (c) Phi-4-14B > 5, Model Openness Conscientiousness Extraversion Agreeableness Neuroticism simple 25.223 28.639 24.925 31, Table 7.

Improvements for AI systems

Bold trait-specific conditioning allows for fine-grained control over the degree of trait expression by embedding explicit numeric values (e.g., 0–100) to each trait dimension into natural language prompts, enabling fine-grained personality control without additional training.
The system can generate personality-aware dialogue agents that maintain consistent behavior across multi-turn interactions, as evidenced by the ability of Big5-Scaler agents to exhibit trait-consistent dialogue generation and maintain consistency through metrics like PersonaCLR.
The framework enables human trait imitation by directly mapping human Big Five scores into Big5-Scaler prompts, allowing agents to generate responses that align with target personality profiles, as suggested by the observation that RMSE values cluster around 1.8 in human imitation experiments.
The model can optimize prompt design for specific architectures and desired outcomes, as empirical analysis shows that shorter prompts and lower trait intensity scales (e.g., n = 10) are most effective under current model capabilities for achieving the lowest root mean squared error in trait alignment.

Abstract

We present Big5-Scaler, a prompt-based framework for conditioning large language models (LLMs) with controllable Big Five personality traits. By embedding numeric trait values into natural language prompts, our method enables fine-grained personality control without additional training. We evaluate Big5-Scaler across trait expression, dialogue generation, and human trait imitation tasks. Results show that it induces consistent and distinguishable personality traits across models, with performance varying by prompt type and scale. Our analysis highlights the effectiveness of concise prompts and lower trait intensities, providing a efficient approach for building personality-aware dialogue agents.