Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders

arXiv:2604.07825v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently emerged as powerful training-free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced information exposure during pretraining, a phenomenon we refer to as knowledge gap problem. To address this, most prior methods have employed a naive uniform augmentation that appends external information for every item in the input prompt. However, this approach not