DST-GFN: A Dual-Stage Transformer Network with Gated Fusion for Pairwise User Preference Prediction in Dialogue Systems

User preference prediction is important for customizing responses in large language models (LLMs) for dialogue systems. This paper presents DST-GFN (Dual-Stage TransformerGated Fusion Network), a model made to predict preferences from two responses generated by LLMs. DST-GFN uses a DualStage Transformer Encoder, a Gated Fusion Block (GFB), and a Hierarchical Contextual Fusion (HCF) layer to find connections and differences between responses. The model processes prompt-response pairs and the relationship between the two responses with two encoders. A gating method then combines the outputs. The final prediction is made using a Softmax layer. It applies a weighted cross-entropy loss and $L 2$ regularization to reduce class imbalance and overfitting. Tests show that DST-GFN performs better than models like BERT, LSTM, and GRU. Its key parts, the Dual-Stage Encoder and Gated Fusion Block, are also shown to be effective. The model can also work in multilingual and multimodal situations.