A Hormone-inspired Emotion Layer for Transformer language models (HELT)
Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fundamentally lack the ability to process and respond to emotional context in a manner analogous to human emotional cognition. Current approaches to emotion modeling in NLP systems rely primarily on discrete emotion classification or simplistic sentiment analysis, which fail to capture the continuous, multi-dimensional nature of human emotional states. In this paper, we introduce HormoneT5, a novel architecture that augments transformer language models with a biologically-inspired Hormone Emotion Block that simulates the human endocrine system's...
| Authors | Eslam Reda, Sara El-Metwally |
|---|---|
| Primary category | cs.NE |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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