Multi-agent systems built from large language model (LLM) agents can improve reasoning through discussion and consensus, but they also risk “herding” effects where homogeneous behavior reinforces shared mistakes, and they often waste tokens through redundant utterances after agreement is effectively reached. This study quantitatively evaluates how explicitly assigning personality traits to LLM agents influences both performance and efficiency in consensus-oriented collaboration. We instantiate three agents with Big Five–based personality profiles, encoded as high/low settings on Extraversion, Conscientiousness, Agreeableness, Openness, and Neuroticism, and compare three team configurations: No Persona (no explicit traits), Same Persona (all agents share one profile), and Different Personas (agents have distinct profiles). Because persona prompting does not guarantee faithful behavioral realization, we introduce a pre-validation step using a Big Five Inventory (BFI) test and iteratively adjust prompts until the intended trait patterns are reflected. Agents then solve multiple-choice questions using a unified, fixed workflow: initial independent answers, three rounds of debate , and majority voting for the final decision. Experiments on MMLU repeat 50 runs per condition (50 questions per run) and evaluate accuracy, token consumption, and answer-change rates that capture beneficial corrections versus harmful shifts. Results show that Different Personas achieves the highest mean accuracy (0.699) and tends to increase correct-change while reducing incorrect-change, suggesting more effective error correction and robustness against misleading arguments. Persona assignment also improves debate efficiency: token usage is highest without personas and lower with persona prompting, although the most accurate heterogeneous configuration does not minimize tokens, indicating a performance–efficiency trade-off. Overall, the findings support treating personality composition as a controllable design variable for collaborative LLM agents.
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- Akikazu Kimura (Autor), 2026, Impact of Introducing Personality Traits into LLM Agents on Collaborative Tasks, Múnich, GRIN Verlag, https://www.grin.com/document/1696419
