LLM agent personality expression and alignment impact user perception in tasks

A new study published on arXiv explores how the personality of conversational agents, powered by large language models, affects user perceptions in goal-oriented tasks. Researchers found that a medium level of personality expression, rather than low or high, led to the most positive user evaluations across various metrics like trust and likeability. Furthermore, aligning the agent's personality with the user's preferences significantly enhanced these positive outcomes, with specific traits like extraversion and emotional stability proving most impactful. AI

IMPACT Optimizing LLM agent personality expression and alignment can improve user experience and adoption in goal-oriented applications.

RANK_REASON Academic paper on LLM-based conversational agent personality and user perception.

Read on arXiv cs.CL →

• arXiv
• Hasibur Rahman
• LLM

• paper
• other

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