LLM agents can adopt personalities in dialogue without losing task focus

Researchers have developed a framework to study how large language models (LLMs) can adopt specific personalities in task-oriented dialogues without sacrificing task completion. The study evaluated GPT-4o, Qwen3-Next-80B, and Gemini 2.0 Flash across various personality traits using the Schema-Guided Dialogue dataset. Findings indicate that while user agents can express personality, system agents can maintain strong task performance, though some traits are less reliably expressed. Adapting the system agent to the user's personality improves aspects like constraint satisfaction and user satisfaction, but introduces a trade-off with truthfulness. AI

IMPACT This research suggests potential for more engaging and personalized user experiences in task-oriented AI systems.

RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Big Five personality traits
• Gemini 2.0 Flash
• GPT-4o
• Hugging Face
• Maryam Shoaeinaeini
• Qwen3 Next 80B
• Schema-Guided Dialogue (SGD) dataset

• paper
• model release

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