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    <abstract>As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tasks, models are now expected to perform well on user-centric measurements involving confidence and human (dis)agreement- factors that reflect subjective preferences. While modeling subjectivity plays an essential role in cognitive science and has been extensively studied, its investigation at the intersection with NLP remains under-explored. In light of this gap, we explore how language models can quantify subjectivity in cognitive appraisal by conducting comprehensive experiments and analyses with both fine-tuned models and prompt-based large language models (LLMs). Our quantitative and qualitative results demonstrate that personality traits and demographic information are critical for measuring subjectivity, yet existing post-hoc calibration methods often fail to achieve satisfactory performance. Furthermore, our in-depth analysis provides valuable insights to guide future research at the intersection of NLP and cognitive science.</abstract>
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%0 Conference Proceedings
%T Modeling Subjectivity in Cognitive Appraisal with Language Models
%A Zhou, Yuxiang
%A Xu, Hainiu
%A Ong, Desmond C.
%A Liakata, Maria
%A Slovak, Petr
%A He, Yulan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhou-etal-2025-modeling
%X As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tasks, models are now expected to perform well on user-centric measurements involving confidence and human (dis)agreement- factors that reflect subjective preferences. While modeling subjectivity plays an essential role in cognitive science and has been extensively studied, its investigation at the intersection with NLP remains under-explored. In light of this gap, we explore how language models can quantify subjectivity in cognitive appraisal by conducting comprehensive experiments and analyses with both fine-tuned models and prompt-based large language models (LLMs). Our quantitative and qualitative results demonstrate that personality traits and demographic information are critical for measuring subjectivity, yet existing post-hoc calibration methods often fail to achieve satisfactory performance. Furthermore, our in-depth analysis provides valuable insights to guide future research at the intersection of NLP and cognitive science.
%R 10.18653/v1/2025.findings-emnlp.744
%U https://aclanthology.org/2025.findings-emnlp.744/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.744
%P 13811-13833

Markdown (Informal)

[Modeling Subjectivity in Cognitive Appraisal with Language Models](https://aclanthology.org/2025.findings-emnlp.744/) (Zhou et al., Findings 2025)

• Modeling Subjectivity in Cognitive Appraisal with Language Models (Zhou et al., Findings 2025)

ACL

• Yuxiang Zhou, Hainiu Xu, Desmond C. Ong, Maria Liakata, Petr Slovak, and Yulan He. 2025. Modeling Subjectivity in Cognitive Appraisal with Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13811–13833, Suzhou, China. Association for Computational Linguistics.