Unveiling Personality Dynamics in AI Dialogue Systems

Prompt-based personality control in large language models (LLMs) is a captivating frontier. Designers aim to create dialogue agents that exhibit consistent personalities across different contexts. Yet, specifying personality traits doesn't guarantee that a language model will embody these traits during interactions.

The Interactionist Perspective

From an interactionist standpoint, personality expression emerges as a context-dependent result. It's influenced by the interplay between specified traits and situational factors. A recent study dives into this complexity, analyzing how perceived Big Five Trait (BFT) expressions vary in LLM-generated dialogues.

Three main factors were examined: personality traits, dialogue roles, and expressive styles. The researchers used a factorial design, generating 1,080 dialogues in both English and Japanese. The dialogues were evaluated using an LLM-as-a-judge framework to estimate expressed Big Five traits in these interactions.

Findings and Implications

The paper's key contribution: expressed personality isn't solely dictated by trait specification. Dialogue role and expressive style significantly impact personality impression. Intriguingly, the influence of these factors varies by trait. Openness is greatly affected by dialogue role, while Conscientiousness and Agreeableness are shaped by expressive style. In contrast, Neuroticism remains primarily driven by trait specification.

So, why does this matter? Even without explicit trait prompts, the social and expressive context can induce distinct personality impressions. This suggests a more nuanced approach is needed in designing AI dialogue systems.

Do these results hold across languages? Broadly, yes. Both English and Japanese dialogues exhibited similar patterns, though differences emerged under certain combinations of personality, role, and style. This highlights the cultural dimensions of AI personality expression.

Rethinking Personality Control

What they did, why it matters, what's missing. The study underscores the need to rethink personality control in LLMs. It's not just about punching in traits. It's about understanding how personality emerges from a blend of traits, roles, and expressions. The ablation study reveals the complexity of this dynamic process.

Why should developers care? For one, it challenges the notion of direct control over AI personalities. Designers must consider the broader context and how it shapes interactions. This builds on prior work from psychology, where personality is seen as a product of both inherent traits and environmental factors.

In a world where AI is increasingly part of our social fabric, understanding these dynamics is essential. How will AI influence our social interactions? Only by decoding these personality dynamics can we hope to create dialogue agents that truly resonate with human users.