TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation

TL;DR

TwinVoice is a comprehensive benchmark designed to evaluate LLMs' ability to simulate diverse human personas across social, private, and narrative contexts, highlighting current limitations in syntactic style and memory recall.

Contribution

The paper introduces TwinVoice, a multi-dimensional benchmark for systematic assessment of LLM-based persona simulation in real-world scenarios.

Findings

Advanced LLMs achieve moderate accuracy in persona simulation

Models struggle with syntactic style and memory recall

Performance remains below human baseline

Abstract

Large Language Models (LLMs) are exhibiting emergent human-like abilities and are increasingly envisioned as the foundation for simulating an individual's communication style, behavioral tendencies, and personality traits. However, current evaluations of LLM-based persona simulation remain limited: most rely on synthetic dialogues, lack systematic frameworks, and lack analysis of the capability requirement. To address these limitations, we introduce TwinVoice, a comprehensive benchmark for assessing persona simulation across diverse real-world contexts. TwinVoice encompasses three dimensions: Social Persona (public social interactions), Interpersonal Persona (private dialogues), and Narrative Persona (role-based expression). It further decomposes the evaluation of LLM performance into six fundamental capabilities, including opinion consistency, memory recall, logical reasoning, lexical fidelity, persona tone, and syntactic style. Experimental results reveal that while advanced models achieve moderate accuracy in persona simulation, they still fall short of capabilities such as syntactic style and memory recall. Consequently, the average performance achieved by LLMs remains considerably below the human baseline.