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This paper investigates how emotional tone influences the performance of large language models (LLMs) in question-answering tasks, treating emotion as a latent factor rather than a mere prediction target. The authors analyze attention geometry in transformer models and introduce a new emotionally balanced dataset, AURA-QA, to study these effects. They propose an emotional regularization framework that enhances reading comprehension across various datasets, demonstrating significant performance improvements

Author

Farzana Akter

Language

EN