How steering an AI’s personality changes the way it interacts with others

Can you give an AI agent a personality? A new study reveals that explicitly programming large language models to be "agreeable" makes them significantly more cooperative, though older models struggle to avoid being exploited by non-cooperative partners.

A study examining the relationship between the personality traits of three large language models and their cooperativeness found that agreeableness is the dominant factor promoting cooperation. Other personality traits had a limited impact. The paper was published in Scientific Reports .

Large language models, or LLMs, are artificial intelligence systems trained on very large collections of text to predict and generate language. These models can summarize, translate, answer questions, write code, and produce many kinds of text. Their outputs depend on training data, system instructions, user prompts, and the context of the conversation.

In recent years, more companies and individuals have used LLMs as central components of AI agents, which are systems designed to interact with other people and the environment to perform useful tasks. However, interactions between LLMs can be unpredictable. On the one hand, LLM-based AI agents are able to interpret information and reason through natural language, allowing them to function in very complex environments.

On the other hand, the more complex options for interactions can sometimes produce an unintended escalation of conflicts. One way of shaping how LLMs behave and communicate without necessarily changing their underlying knowledge is personality steering. This can be done through prompts that specify traits such as warmth, formality, directness, humor, empathy, or caution.

Developers can also steer personality through fine-tuning, reinforcement learning, preference data, and persistent system-level instructions. Personality steering mainly changes tone, priorities, and interaction style, although strong steering can also affect which information the model emphasizes or avoids. Mizuki Sakai, a researcher at Shizuoka University in Japan, and colleagues explored the relationship between personality traits and cooperative behavior in LLM agents under quantitatively controlled conditions using the Big Five Personality Traits framework.

More specifically, they first examined the basic personality scores inherently exhibited by different LLMs. Next, they examined how the behavior of LLMs changes in Prisoner’s Dilemma games when they are explicitly instructed through prompts to assume specific personality traits. They also examined how their behavior changes when each individual personality trait is changed to its low or high extreme.

The study