Modeling Subjectivity in Cognitive Appraisal with Language Models

• URL: http://arxiv.org/abs/2503.11381v1
• Date: Fri, 14 Mar 2025 13:25:41 GMT
• Title: Modeling Subjectivity in Cognitive Appraisal with Language Models
• Authors: Yuxiang Zhou, Hainiu Xu, Desmond C. Ong, Petr Slovak, Yulan He,
• Abstract summary: We show how language models can harness subjectivity by conducting comprehensive experiments and analysis across various scenarios.<n>Our findings reveal that personality traits and demographical information are critical for measuring subjectivity.
• Score: 16.846297851557477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the utilization of language models in interdisciplinary, human-centered studies grow, the expectation of model capabilities continues to evolve. Beyond excelling at conventional tasks, models are recently expected to perform well on user-centric measurements involving confidence and human (dis)agreement -- factors that reflect subjective preferences. While modeling of subjectivity plays an essential role in cognitive science and has been extensively studied, it remains under-explored within the NLP community. In light of this gap, we explore how language models can harness subjectivity by conducting comprehensive experiments and analysis across various scenarios using both fine-tuned models and prompt-based large language models (LLMs). Our quantitative and qualitative experimental results indicate that existing post-hoc calibration approaches often fail to produce satisfactory results. However, our findings reveal that personality traits and demographical information are critical for measuring subjectivity. Furthermore, our in-depth analysis offers valuable insights for future research and development in the interdisciplinary studies of NLP and cognitive science.

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