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    <abstract>As large language models are increasingly deployed to simulate diverse human characters, ensuring persona fidelity, defined as the extent to which an agent’s behavior consistently reflects the psychological and stylistic characteristics of a target persona, has become a critical requirement. However, existing evaluation paradigms primarily rely on either holistic LLM-based judges, which are prone to “holistic appraisal hallucination”, or static psychometric inventories, which fail to capture the context-dependent fidelity required in dynamic dialogue. To address these limitations, we propose PRISM (Persona Reasoning with Inverse SFL-based Modeling), a psycholinguistically grounded framework that reformulates persona fidelity evaluation as a structured inverse inference task. Inspired by Systemic Functional Linguistics (SFL), PRISM decomposes persona fidelity into three functional dimensions: Task Framing, Interpersonal Stance, and Linguistic Style. It estimates dimension-specific evidence over a persona-conditioned label space and aggregates these signals into an interpretable and auditable evaluation process. Experiments show that PRISM yields more accurate and stable judgements than traditional holistic judging, providing a more reliable framework for persona fidelity evaluation.</abstract>
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%0 Conference Proceedings
%T Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference
%A Li, Mengfan
%A Wei, Zesheng
%A Shi, Xuanhua
%A Deng, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-llms-understand
%X As large language models are increasingly deployed to simulate diverse human characters, ensuring persona fidelity, defined as the extent to which an agent’s behavior consistently reflects the psychological and stylistic characteristics of a target persona, has become a critical requirement. However, existing evaluation paradigms primarily rely on either holistic LLM-based judges, which are prone to “holistic appraisal hallucination”, or static psychometric inventories, which fail to capture the context-dependent fidelity required in dynamic dialogue. To address these limitations, we propose PRISM (Persona Reasoning with Inverse SFL-based Modeling), a psycholinguistically grounded framework that reformulates persona fidelity evaluation as a structured inverse inference task. Inspired by Systemic Functional Linguistics (SFL), PRISM decomposes persona fidelity into three functional dimensions: Task Framing, Interpersonal Stance, and Linguistic Style. It estimates dimension-specific evidence over a persona-conditioned label space and aggregates these signals into an interpretable and auditable evaluation process. Experiments show that PRISM yields more accurate and stable judgements than traditional holistic judging, providing a more reliable framework for persona fidelity evaluation.
%U https://aclanthology.org/2026.emnlp-main.952/
%P 21946-21965

Markdown (Informal)

[Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference](https://aclanthology.org/2026.emnlp-main.952/) (Li et al., EMNLP 2026)

• Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference (Li et al., EMNLP 2026)

ACL

• Mengfan Li, Zesheng Wei, Xuanhua Shi, and Yang Deng. 2026. Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 21946–21965, Budapest, Hungary. Association for Computational Linguistics.