一种受激素启发的情感层用于Transformer语言模型（HELT）
A Hormone-inspired Emotion Layer for Transformer language models (HELT)
AI总结：
本文提出HELT，通过生物启发的激素情感模块增强Transformer模型，以模拟人类情感处理。通过六种连续激素值模拟，提升模型对情感上下文的响应能力，实验表明在情感准确性与同理质量上优于基线模型。
摘要：
大型语言模型在生成上下文相关且语法正确的文本方面表现出色，但缺乏处理和响应情感上下文的能力。本文提出HormoneT5，通过生物启发的激素情感模块增强Transformer模型。该模块通过六个连续激素值模拟人类内分泌系统在情感处理中的作用。通过专门设计的每种激素注意力头计算这些值，使用正交初始化的可学习查询、温度缩放注意力机制和深度输出投影。这些激素值被转换为情感嵌入，调节编码器隐藏状态，以生成合适的情感响应。我们提出多目标训练框架，结合序列到序列损失、激素预测损失和多样性正则化，防止注意力崩溃。实验结果表明，HormoneT5在定制的情感标注数据集上实现了85%以上的每种激素准确度，激素区分范围超过0.85。人类评估研究显示，HormoneT5生成的响应在情感合适性和同理质量上显著优于基线T5输出。
英文摘要：
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 role in emotional processing. Our approach computes six continuous hormone-like values through specialized per-hormone attention heads, each with orthogonally initialized learnable queries, temperature-scaled attention mechanisms, and deep output projections. These hormone values are then transformed into an emotional embedding that modulates the encoder hidden states, enabling emotionally-appropriate response generation. We propose a multi-objective training framework combining sequence-to-sequence loss, hormone prediction loss with margin penalties, and diversity regularization to prevent attention collapse. Experimental results on our curated emotion-labeled dataset demonstrate that HormoneT5 achieves 85%+ per-hormone accuracy within a 0.15 tolerance threshold, with hormone differentiation ranges exceeding 0.85 across all six hormones between contrasting emotional tones. Human evaluation studies show significant preference (p < 0.01) for HormoneT5-generated responses in terms of emotional appropriateness and empathetic quality compared to baseline T5 outputs. Our work opens new directions for biologically-grounded affective computing and emotionally intelligent conversational agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
