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        <title>Persona Switch: Mixing Distinct Perspectives in Decoding Time</title>
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        <namePart type="given">Junseok</namePart>
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    <abstract>Role-play prompting is known to steer the behavior of language models by injecting a persona into the prompt, improving their zero-shot reasoning capabilities. However, such improvements are inconsistent across different tasks or instances. This inconsistency suggests that zero-shot and role-play prompting may offer complementary strengths rather than one being universally superior. Building on this insight, we propose **Persona Switch**, a novel decoding method that dynamically combines the benefits of both prompting strategies. Our method proceeds step-by-step, selecting the better output between zero-shot and role-play prompting at each step by comparing their output confidence, as measured by the logit gap. Experiments with widely-used LLMs demonstrate that Persona Switch consistently outperforms competitive baselines, achieving up to 5.13% accuracy improvement. Furthermore, we show that output confidence serves as an informative measure for selecting the more reliable output.</abstract>
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        <date>2026-03</date>
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            <start>1955</start>
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%0 Conference Proceedings
%T Persona Switch: Mixing Distinct Perspectives in Decoding Time
%A Kim, Junseok
%A Yang, Nakyeong
%A Jung, Kyomin
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F kim-etal-2026-persona
%X Role-play prompting is known to steer the behavior of language models by injecting a persona into the prompt, improving their zero-shot reasoning capabilities. However, such improvements are inconsistent across different tasks or instances. This inconsistency suggests that zero-shot and role-play prompting may offer complementary strengths rather than one being universally superior. Building on this insight, we propose **Persona Switch**, a novel decoding method that dynamically combines the benefits of both prompting strategies. Our method proceeds step-by-step, selecting the better output between zero-shot and role-play prompting at each step by comparing their output confidence, as measured by the logit gap. Experiments with widely-used LLMs demonstrate that Persona Switch consistently outperforms competitive baselines, achieving up to 5.13% accuracy improvement. Furthermore, we show that output confidence serves as an informative measure for selecting the more reliable output.
%U https://aclanthology.org/2026.findings-eacl.101/
%P 1955-1967

Markdown (Informal)

[Persona Switch: Mixing Distinct Perspectives in Decoding Time](https://aclanthology.org/2026.findings-eacl.101/) (Kim et al., Findings 2026)

• Persona Switch: Mixing Distinct Perspectives in Decoding Time (Kim et al., Findings 2026)

ACL

• Junseok Kim, Nakyeong Yang, and Kyomin Jung. 2026. Persona Switch: Mixing Distinct Perspectives in Decoding Time. In Findings of the Association for Computational Linguistics: EACL 2026, pages 1955–1967, Rabat, Morocco. Association for Computational Linguistics.