@inproceedings{lee-etal-2025-spectrum,
title = "{SP}e{C}trum: A Grounded Framework for Multidimensional Identity Representation in {LLM}-Based Agent",
author = "Lee, Keyeun and
Kim, Seo Hyeong and
Lee, Seolhee and
Eun, Jinsu and
Ko, Yena and
Jeon, Hayeon and
Kim, Esther Hehsun and
Cho, Seonghye and
Yang, Soeun and
Kim, Eun-mee and
Lim, Hajin",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.356/",
doi = "10.18653/v1/2025.naacl-long.356",
pages = "6971--6991",
ISBN = "979-8-89176-189-6",
abstract = "Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual{'}s multidimensional self-concept. SPeCtrum integrates three core components: Social Identity (S), Personal Identity (P), and Personal Life Context (C), each contributing distinct yet interconnected aspects of identity. To evaluate SPeCtrum{'}s effectiveness in identity representation, we conducted automated and human evaluations. Automated evaluations using popular drama characters showed that Personal Life Context (C){---}derived from short essays on preferences and daily routines{---}modeled characters' identities more effectively than Social Identity (S) and Personal Identity (P) alone and performed comparably to the full SPC combination. In contrast, human evaluations involving real-world individuals found that the full SPC combination provided a more comprehensive self-concept representation than C alone. Our findings suggest that while C alone may suffice for basic identity simulation, integrating S, P, and C enhances the authenticity and accuracy of real-world identity representation. Overall, SPeCtrum offers a structured approach for simulating individuals in LLM agents, enabling more personalized human-AI interactions and improving the realism of simulation-based behavioral studies."
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<abstract>Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual’s multidimensional self-concept. SPeCtrum integrates three core components: Social Identity (S), Personal Identity (P), and Personal Life Context (C), each contributing distinct yet interconnected aspects of identity. To evaluate SPeCtrum’s effectiveness in identity representation, we conducted automated and human evaluations. Automated evaluations using popular drama characters showed that Personal Life Context (C)—derived from short essays on preferences and daily routines—modeled characters’ identities more effectively than Social Identity (S) and Personal Identity (P) alone and performed comparably to the full SPC combination. In contrast, human evaluations involving real-world individuals found that the full SPC combination provided a more comprehensive self-concept representation than C alone. Our findings suggest that while C alone may suffice for basic identity simulation, integrating S, P, and C enhances the authenticity and accuracy of real-world identity representation. Overall, SPeCtrum offers a structured approach for simulating individuals in LLM agents, enabling more personalized human-AI interactions and improving the realism of simulation-based behavioral studies.</abstract>
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%0 Conference Proceedings
%T SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent
%A Lee, Keyeun
%A Kim, Seo Hyeong
%A Lee, Seolhee
%A Eun, Jinsu
%A Ko, Yena
%A Jeon, Hayeon
%A Kim, Esther Hehsun
%A Cho, Seonghye
%A Yang, Soeun
%A Kim, Eun-mee
%A Lim, Hajin
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F lee-etal-2025-spectrum
%X Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual’s multidimensional self-concept. SPeCtrum integrates three core components: Social Identity (S), Personal Identity (P), and Personal Life Context (C), each contributing distinct yet interconnected aspects of identity. To evaluate SPeCtrum’s effectiveness in identity representation, we conducted automated and human evaluations. Automated evaluations using popular drama characters showed that Personal Life Context (C)—derived from short essays on preferences and daily routines—modeled characters’ identities more effectively than Social Identity (S) and Personal Identity (P) alone and performed comparably to the full SPC combination. In contrast, human evaluations involving real-world individuals found that the full SPC combination provided a more comprehensive self-concept representation than C alone. Our findings suggest that while C alone may suffice for basic identity simulation, integrating S, P, and C enhances the authenticity and accuracy of real-world identity representation. Overall, SPeCtrum offers a structured approach for simulating individuals in LLM agents, enabling more personalized human-AI interactions and improving the realism of simulation-based behavioral studies.
%R 10.18653/v1/2025.naacl-long.356
%U https://aclanthology.org/2025.naacl-long.356/
%U https://doi.org/10.18653/v1/2025.naacl-long.356
%P 6971-6991
Markdown (Informal)
[SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent](https://aclanthology.org/2025.naacl-long.356/) (Lee et al., NAACL 2025)
ACL
- Keyeun Lee, Seo Hyeong Kim, Seolhee Lee, Jinsu Eun, Yena Ko, Hayeon Jeon, Esther Hehsun Kim, Seonghye Cho, Soeun Yang, Eun-mee Kim, and Hajin Lim. 2025. SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6971–6991, Albuquerque, New Mexico. Association for Computational Linguistics.