Shaping Model Behavior (Style, Persona) via Synthetic Inputs

While fine-tuning an LLM for a specific task ensures it knows what to respond, shaping its behavior through style and persona dictates how it responds. This is a significant step towards creating LLMs that are not just functional but also align with specific communication goals, brand identities, or character roles. Synthetic data proves particularly effective, as it allows for the deliberate creation of examples embodying the desired stylistic traits, which might be scarce or inconsistently represented in naturally occurring datasets.

Defining Style and Persona

Before we generate data, let's clarify what we mean by "style" and "persona" in the context of LLMs:

• Style refers to the linguistic characteristics of the LLM's output. This includes aspects like:

  • Formality: Is the language formal (e.g., "It is imperative to consider...") or informal (e.g., "We've gotta think about...")?
  • Tone: Is the LLM's attitude serious, humorous, empathetic, neutral, enthusiastic, or cautious?
  • Vocabulary: Does it use simple, everyday words, or specialized jargon? Is the vocabulary rich and varied or concise and direct?
  • Sentence Structure: Are sentences long and complex, or short and punchy?
  • Use of Rhetorical Devices: Does it employ metaphors, similes, humor, or other literary techniques?

  Here's a brief comparison of stylistic features:

  Feature	Formal Style	Casual Style	Empathetic Tone
  Vocabulary	Precise, potentially academic	Colloquial, common	Understanding, gentle
  Sentence Str.	Often complex, grammatically strict	Simpler, more direct	Often softer, reassuring
  Contractions	Generally avoided	Frequently used	Used naturally
  Addressing	"One might consider", "The user"	"You might want to", "You"	"I understand how you feel"
  Overall Feel	Objective, respectful, distant	Friendly, approachable	Caring, supportive

• Persona is a more comprehensive concept. It encompasses style but also includes a consistent set of traits, beliefs, knowledge (or lack thereof within certain domains for character play), and interaction patterns that define a "character" for the LLM. For example, a persona could be:

  • A "helpful teaching assistant" who is patient, encouraging, and breaks down complex topics simply.
  • A "sarcastic robot sidekick" who provides correct information but with a dry, witty edge.
  • A "17th-century pirate captain" who explains modern concepts using archaic language and pirate slang.

Shaping persona often means the LLM maintains a consistent "personality" across many interactions, potentially referencing its own defined characteristics or backstory.

Generating Synthetic Data for Behavior Shaping

Creating synthetic data to instill a specific style or persona involves crafting examples where the LLM's responses consistently exhibit the target characteristics. Here are common approaches:

1. Direct Persona Prompting for Content Generation: This is one of the most straightforward methods. You instruct a capable LLM (a "teacher" model) to generate responses as if it already embodies the target persona or style.

   For instance, to generate data for a "wise old scholar" persona:

   • Instruction to Teacher LLM: "You are a wise old scholar, deeply knowledgeable and slightly formal, but always kind. Generate a response to the following user query: 'Can you explain the concept of photosynthesis in simple terms?'"
   • The output from the teacher LLM becomes your synthetic data point:

     {
       "instruction": "Can you explain the concept of photosynthesis in simple terms?",
       "output": "Ah, a most excellent inquiry, young seeker of knowledge. Photosynthesis, in its essence, is the marvelous process by which our green-leafed friends, the plants, partake of sunlight, water, and the very air we exhale to create their own sustenance, and in doing so, graciously provide the oxygen that sustains us. A truly wondrous cycle, is it not?"
     }
     

   You would repeat this process with many varied user queries to build a dataset.

2. Few-Shot Exemplar-Based Generation: Provide the teacher LLM with a few high-quality examples (shots) of the desired style or persona in action, and then ask it to generate more examples for new instructions.

   • Prompt to Teacher LLM:

     Here are examples of how a 'super enthusiastic sports commentator' responds:
     User: What happened in the game last night?
     Commentator: WOAH! WHAT A GAME! The Wildcats CLAWED their way back in the final SECONDS! Unbelievable scenes! You HAD to be there!
     
     User: Tell me about the weather.
     Commentator: The weather? Who cares about the weather when there's THIS much ACTION on the field! But okay, if you INSIST, it's looking like a GOOOOAAAL of a sunny day!
     
     Now, respond to this user query as the 'super enthusiastic sports commentator':
     User: Can you recommend a good book?
     

   The LLM's new response, following the demonstrated style, becomes another data point.

3. Style-Focused Paraphrasing or Rewriting: Take existing instruction-response pairs (which might be neutral in style) and use an LLM to rewrite the responses to fit the target style or persona.

   • Original Data:

     {"instruction": "How do I bake a cake?", "output": "First, preheat your oven. Then, mix flour, sugar, eggs, and butter. Pour into a pan and bake for 30 minutes."}
     

   • Instruction to Teacher LLM: "Rewrite the following cake baking instructions in the style of a very prim and proper British butler who is slightly exasperated by the simplicity of the task."
   • The rewritten output forms the new synthetic example.

4. Role-Playing Scenarios: Set up scenarios where an LLM is instructed to play the role of the target persona interacting with a user (which could be another LLM or predefined prompts). The dialogue generated by the persona-LLM becomes training data. This is particularly useful for developing conversational personas that need to maintain consistency over multiple turns.

Structuring Data for Style and Persona Fine-Tuning

The data format for fine-tuning typically remains the same as for other instruction fine-tuning tasks, commonly JSONL files with instruction (or prompt) and output (or completion) fields. The important thing is that the output field consistently reflects the desired style or persona for a given instruction.

[
  {"instruction": "What's the capital of Singapore?", "output": "Arr, matey! Singapore itself be the capital, a shining jewel of an island, savvy?"},
  {"instruction": "Explain black holes.", "output": "Shiver me timbers! A black hole be a fearsome abyss in the starry sea, where not even light can escape its clutches! Best steer clear, lest ye be swallowed whole!"}
]

When an LLM is fine-tuned on a dataset composed entirely of such pairs, it learns to adopt the "pirate captain" persona for any instruction it receives.

The following diagram illustrates the general workflow for generating persona-specific synthetic data for fine-tuning:

This diagram shows how initial guidelines or seed prompts are used by a generator LLM to produce a dataset of instruction-response pairs. This dataset then serves as the training material to fine-tune a base LLM, resulting in a model that consistently exhibits the desired persona.

Important Notes

When using synthetic data to shape model behavior, keep these points in mind:

• Consistency is Important: The synthetic dataset must consistently show the desired persona or style across a wide variety of instructions. Inconsistencies can confuse the model during fine-tuning, leading to an unreliable persona.
• Quality over Sheer Quantity: While a decent volume of data is needed, a smaller set of high-quality, highly consistent examples is often more effective than a massive dataset with mixed or weak persona signals.
• Avoid "Persona Override": The persona should not overshadow the model's ability to perform its core task accurately. For instance, a helpful assistant persona should still provide correct information, even if delivered in a specific style. The underlying capabilities must be preserved. This often involves ensuring your synthetic data includes examples of the persona handling factual queries correctly.
• Subtlety: Creating a truly believable and non-caricatured persona is an art. Overly strong or simplistic personas can feel artificial. Iterative refinement of your synthetic data generation prompts is often necessary.
• Evaluation Challenges: Quantitatively measuring "how well" a model has adopted a persona can be difficult. While you can look for stylistic markers (e.g., use of certain phrases), human evaluation is frequently the most reliable way to assess the naturalness and consistency of the persona.
• Balancing with General Capabilities: If the fine-tuning dataset is exclusively persona-driven for every type of input, the model might apply that persona even when it's inappropriate (e.g., in a safety-critical or highly formal context where the persona is playful). Consider strategies like mixing persona data with neutral data or using conditional prompting at inference time if flexibility is needed.

By carefully generating synthetic datasets that exemplify the desired style and persona, you can guide your LLMs to interact in ways that are more engaging, brand-aligned, and tailored to specific application requirements. This goes further than mere information retrieval, allowing LLMs to become more effective and relatable communicators.