How to Crack the Black Box of Emotion Generation? Interpretable Dual-Path Function Vectors for Token-Level Control

Existing empathetic dialogue generation methods face limitations in the fine-grained control and cross-modal transfer of emotion expression, while generally lacking interpretability into the models’ internal emotion generation mechanisms. We propose an interpretable dual-path emotional functional vector framework, integrated with a token-level adaptive injection strategy, to achieve transparent and controllable empathetic dialogue generation. Specifically, we design a mean difference pathway to extract semantic emotional features and a singular value decomposition pathway to extract parameter-structural emotion features, achieving dual-path fusion via adaptive weights. The token-level strategy dynamically assigns regulation weights based on an attention mechanism, matching the differential contributions of different words in emotional transmission. Additionally, we construct a text-to-speech cross-modal evaluation system to verify the consistency of emotional information across multimodal scenarios.Experimental results on the ED and ESConv datasets demonstrate that the proposed method significantly outperforms all existing state-of-the-art baselines. When using Llama-3.1 as the backbone model, we achieve a 18.22% improvement in Acc2 and a 6.02% improvement in F_Bert. Our work provides an interpretable and efficient solution for controllable emotion generation in LLMs.