Papers by Anahita Bolourani
Mechanistic Interpretability of Emotion Inference in Large Language Models (2025.findings-acl)
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| Challenge: | Existing studies on large language models (LLMs) show promising capabilities in predicting human emotions from text. |
| Approach: | They investigate how autoregressive LLMs infer emotions by focusing on appraisal theory . they show that emotion representations are functionally localized to specific regions in the model . |
| Outcome: | The proposed model is functionally localized to specific regions in the model, and the results align with theoretical and intuitive expectations. |
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness (2026.acl-long)
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Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani, Anahita Bolourani, Leonardo Blas, Emilio Ferrara, Jonathan Gratch, Sai Praneeth Karimireddy
| Challenge: | Using a model with a high degree of emotion and personality control, large language models can be used to control socially interactive interactions. |
| Approach: | They propose a Psychologically-informed benchmark to evaluate LLM steering effectiveness and trustworthiness across emotion and personality domains. |
| Outcome: | The framework establishes the first holistic evaluation of emotion and personality steering, offering insights into its interpretability and reliability for socially interactive applications. |