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    <abstract>Role-playing large language models (LLMs) are expected to adopt a character’s style while also respecting that character’s knowledge boundaries. Prior evaluations detect character hallucination but rarely distinguish whether errors arise from failure to recognize a boundary or from failure to comply despite recognition. We introduce CHARM, a multicultural benchmark of 40 real and fictional characters drawn from five cultural-linguistic regions, and validated by native reviewers. It probes two boundary types, Temporal (historical vs. modern) and Cross-Universe (entities outside a character’s narrative or historical universe), using abstention-enabled multiple-choice questions. We propose a two-stage evaluation that separates Boundary-Awareness (explicit recognition that a query is out of scope) from Boundary-Compliance (abstention when answering concrete questions). Evaluations across six LLMs show that hallucination is driven predominantly by compliance failures. Models frequently acknowledge that a query lies outside the character’s knowledge yet still provide factual, out-of-character answers. By re-posing the same questions to the target character, we confirm that a large fraction of these cases are verified parametric overrides; the model stores the relevant fact but fails to suppress it. We also observe systematic cultural variation in these failures, consistent with imbalances in how characters from different regions are represented in model knowledge.</abstract>
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%0 Conference Proceedings
%T CHARM: Character Hallucination for Multicultural Role Play Benchmark
%A Han, Sunkyung
%A Park, Nahyeon
%A Seo, Gaeun
%A Yoon, Seunghyun
%A Bak, JinYeong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F han-etal-2026-charm
%X Role-playing large language models (LLMs) are expected to adopt a character’s style while also respecting that character’s knowledge boundaries. Prior evaluations detect character hallucination but rarely distinguish whether errors arise from failure to recognize a boundary or from failure to comply despite recognition. We introduce CHARM, a multicultural benchmark of 40 real and fictional characters drawn from five cultural-linguistic regions, and validated by native reviewers. It probes two boundary types, Temporal (historical vs. modern) and Cross-Universe (entities outside a character’s narrative or historical universe), using abstention-enabled multiple-choice questions. We propose a two-stage evaluation that separates Boundary-Awareness (explicit recognition that a query is out of scope) from Boundary-Compliance (abstention when answering concrete questions). Evaluations across six LLMs show that hallucination is driven predominantly by compliance failures. Models frequently acknowledge that a query lies outside the character’s knowledge yet still provide factual, out-of-character answers. By re-posing the same questions to the target character, we confirm that a large fraction of these cases are verified parametric overrides; the model stores the relevant fact but fails to suppress it. We also observe systematic cultural variation in these failures, consistent with imbalances in how characters from different regions are represented in model knowledge.
%U https://aclanthology.org/2026.findings-emnlp.460/
%P 9659-9674

Markdown (Informal)

[CHARM: Character Hallucination for Multicultural Role Play Benchmark](https://aclanthology.org/2026.findings-emnlp.460/) (Han et al., Findings 2026)

• CHARM: Character Hallucination for Multicultural Role Play Benchmark (Han et al., Findings 2026)

ACL

• Sunkyung Han, Nahyeon Park, Gaeun Seo, Seunghyun Yoon, and JinYeong Bak. 2026. CHARM: Character Hallucination for Multicultural Role Play Benchmark. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 9659–9674, Budapest, Hungary. Association for Computational Linguistics.