@inproceedings{kong-etal-2025-sharp,
title = "{SHARP}: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing {LLM}s",
author = "Kong, Chuyi and
Luo, Ziyang and
Lin, Hongzhan and
Fan, Zhiyuan and
Fan, Yaxin and
Sun, Yuxi and
Ma, Jing",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.47/",
doi = "10.18653/v1/2025.findings-acl.47",
pages = "839--866",
ISBN = "979-8-89176-256-5",
abstract = "The advanced role-playing capabilities of Large Language Models (LLMs) have enabled rich interactive scenarios, yet existing research in social interactions neglects hallucination while struggling with poor generalizability and implicit character fidelity judgments. To bridge this gap, motivated by human behaviour, we introduce a generalizable and explicit paradigm for uncovering interactive patterns of LLMs across diverse worldviews. Specifically, we first define interactive hallucination through stance transfer, then construct SHARP, a benchmark built by extracting relations from commonsense knowledge graphs and utilizing LLMs' inherent hallucination properties to simulate multi-role interactions. Extensive experiments confirm our paradigm{'}s effectiveness and stability, examine the factors that influence these metrics, and challenge conventional hallucination mitigation solutions. More broadly, our work reveals a fundamental limitation in popular post-training methods for role-playing LLMs: the tendency to obscure knowledge beneath style, resulting in monotonous yet human-like behaviors{---}interactive hallucination."
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%0 Conference Proceedings
%T SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs
%A Kong, Chuyi
%A Luo, Ziyang
%A Lin, Hongzhan
%A Fan, Zhiyuan
%A Fan, Yaxin
%A Sun, Yuxi
%A Ma, Jing
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F kong-etal-2025-sharp
%X The advanced role-playing capabilities of Large Language Models (LLMs) have enabled rich interactive scenarios, yet existing research in social interactions neglects hallucination while struggling with poor generalizability and implicit character fidelity judgments. To bridge this gap, motivated by human behaviour, we introduce a generalizable and explicit paradigm for uncovering interactive patterns of LLMs across diverse worldviews. Specifically, we first define interactive hallucination through stance transfer, then construct SHARP, a benchmark built by extracting relations from commonsense knowledge graphs and utilizing LLMs’ inherent hallucination properties to simulate multi-role interactions. Extensive experiments confirm our paradigm’s effectiveness and stability, examine the factors that influence these metrics, and challenge conventional hallucination mitigation solutions. More broadly, our work reveals a fundamental limitation in popular post-training methods for role-playing LLMs: the tendency to obscure knowledge beneath style, resulting in monotonous yet human-like behaviors—interactive hallucination.
%R 10.18653/v1/2025.findings-acl.47
%U https://aclanthology.org/2025.findings-acl.47/
%U https://doi.org/10.18653/v1/2025.findings-acl.47
%P 839-866
Markdown (Informal)
[SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs](https://aclanthology.org/2025.findings-acl.47/) (Kong et al., Findings 2025)
ACL