How do LLM agent personalities affect task selection?
Personality-Driven Decision-Making in LLM-Based Autonomous Agents
April 2, 2025
https://arxiv.org/pdf/2504.00727This research explores how assigning personality traits to LLM-based autonomous agents (using the OCEAN model – Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) affects their decision-making process, specifically task selection and prioritization within pre-planned schedules.
Key points for LLM-based multi-agent systems:
- Personality induction significantly influences task selection: Agents prioritize tasks aligning with their assigned traits (e.g., conscientious agents prioritize work-related tasks).
- Prompt engineering is key: Carefully crafted prompts effectively induce desired personality traits in LLMs without model parameter adjustments.
- Non-determinism is present but aligned with the persona: Even with identical prompts and induced personalities, agent behavior shows variations, but these variations remain generally consistent with the given personality.
- Advanced LLMs exhibit stronger personality effects: More sophisticated LLMs like GPT-4 demonstrate a greater capacity for reflecting induced personality traits compared to simpler models.
- Sampling temperature impacts determinism: Higher sampling temperatures introduce more randomness in task selection, potentially overriding the influence of the induced persona.
- Ethical considerations are paramount: The ability to induce personality traits raises ethical concerns regarding potential misuse for misinformation or manipulation, necessitating careful oversight.