@inproceedings{zhang-etal-2025-revealing,
title = "Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in {LLM} Role-Playing",
author = "Zhang, Wenyuan and
Nie, Shuaiyi and
Sheng, Jiawei and
Zhang, Zefeng and
Zhang, Xinghua and
He, Yongquan and
Liu, Tingwen",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1689/",
pages = "33267--33290",
ISBN = "979-8-89176-332-6",
abstract = "Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, existing works usually overlook the exploration of LLMs' ability to detect characters' known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus. In this paper, we propose RoleKE-Bench to evaluate LLMs' ability to detect errors in KKE and UKE. The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge. We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S$^2$RD), to explore further the potential for improving error detection capabilities."
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<abstract>Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, existing works usually overlook the exploration of LLMs’ ability to detect characters’ known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus. In this paper, we propose RoleKE-Bench to evaluate LLMs’ ability to detect errors in KKE and UKE. The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge. We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S²RD), to explore further the potential for improving error detection capabilities.</abstract>
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%0 Conference Proceedings
%T Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing
%A Zhang, Wenyuan
%A Nie, Shuaiyi
%A Sheng, Jiawei
%A Zhang, Zefeng
%A Zhang, Xinghua
%A He, Yongquan
%A Liu, Tingwen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-revealing
%X Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, existing works usually overlook the exploration of LLMs’ ability to detect characters’ known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus. In this paper, we propose RoleKE-Bench to evaluate LLMs’ ability to detect errors in KKE and UKE. The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge. We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S²RD), to explore further the potential for improving error detection capabilities.
%U https://aclanthology.org/2025.emnlp-main.1689/
%P 33267-33290
Markdown (Informal)
[Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing](https://aclanthology.org/2025.emnlp-main.1689/) (Zhang et al., EMNLP 2025)
ACL