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Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path: privileged information extracted from experience is provided to a teacher, whose behavior is then distilled into a student model that has no access...
Graph-Language Models (GLMs) aim to endow LLMs with structure-grounded reasoning ability, yet existing solutions often struggle with modality interference : structural information can disrupt pretrained linguistic reasoning, while language cues can overwhelm structural signals. Mainstream modular GLMs with an external...
Preference-Aligned Test-Time Training (P-TTT) is proposed, which explicitly encodes preference relations conveyed by contextual pairs into user-specific fast weights for personalized reward prediction and outperforms state-of-the-art methods by a large margin.
LBR is proposed, a lightweight and model-agnostic framework for mitigating length bias in LLM-based recommendation that substantially alleviates length bias while consistently improving recommendation accuracy and fairness, with negligible additional training and inference overhead.
GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations that are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder...
SmartGR is proposed, a novel distillation framework that utilizes Hierarchy-Aware SID Distillation to transfer the teacher's modeling capability across the hierarchy and leverages Beam-Aware Ranking Distillation to distill the teacher's ranking preferences during beam search.
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