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This paper presents PersonaGym, a dynamic evaluation framework, and PersonaScore, an automatic evaluation metric based on decision theory. It addresses the problem of assessing the degree of persona adherence of a persona agent (a LLM agent conditioned to act according to a specific persona) in a free-form setting where consistency must be maintained across diverse environments. Evaluating ten leading LLMs with 200 personas and 10,000 questions reveals that model size and complexity do not necessarily correlate with persona agent performance, highlighting the need for algorithmic and architectural innovations for faithful and high-performing persona agents. For example, GPT-4.1 and LLaMA-3-8b achieved identical PersonaScores.
Takeaways, Limitations
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Takeaways:
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PersonaGym and PersonaScore provide new frameworks and metrics to comprehensively evaluate the performance of persona agents.
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We demonstrate that the size and complexity of large-scale language models do not guarantee the performance of persona agents, suggesting future research directions.
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It suggests the potential for the development of persona agents in various fields such as education and healthcare.
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Limitations:
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Additional validation of PersonaScore's human alignment method may be needed.
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The type and scope of LLMs used in the evaluation may be limited.
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Further research may be needed to fully address the complexities of assessing persona consistency in free-form settings.
