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 only wastes limited context budget on redundant augmentation for well-known items but can also hinder the model's effective reasoning. To this end, we propose KnowSA_CKP (Knowledge-aware Selective Augmentation with Comparative Knowledge Probing) to mitigate the knowledge gap problem. KnowSA_CKP estimates the LLM's internal knowledge by evaluating its capability to capture collaborative relationships and selectively injects additional information only where it is most needed. By avoiding unnecessary augmentation for well-known items, KnowSA_CKP focuses on items that benefit most from knowledge supplementation, thereby making more effective use of the context budget. KnowSA_CKP requires no fine-tuning step, and consistently improves both recommendation accuracy and context efficiency across four real-world datasets.

翻译：大型语言模型（LLMs）近期已成为强大的免训练推荐器。然而，由于预训练阶段信息暴露不均衡，其对单个物品的知识必然存在不均衡，我们将这一现象称为知识缺口问题。为解决此问题，以往方法大多采用朴素统一增强策略，即在输入提示中为每个物品附加外部信息。但该方法不仅会将有限的上下文预算浪费在冗余增强已知物品上，还可能阻碍模型的有效推理。为此，我们提出KnowSA_CKP（基于比较知识探测的知识感知选择性增强）以缓解知识缺口问题。KnowSA_CKP通过评估模型捕捉协作关系的能力来估算LLM的内部知识，并仅在最需要的环节选择性注入附加信息。通过避免对已知物品进行不必要的增强，KnowSA_CKP聚焦于最能从知识补充中获益的物品，从而更有效地利用上下文预算。KnowSA_CKP无需微调步骤，在四个真实数据集上持续提升了推荐准确性与上下文效率。