Bearing in mind the limited parametric knowledge of Large Language Models (LLMs), retrieval-augmented generation (RAG) which supplies them with the relevant external knowledge has served as an approach to mitigate the issue of hallucinations to a certain extent. However, uniformly retrieving supporting context makes response generation source-inefficient, as triggering the retriever is not always necessary, or even inaccurate, when a model gets distracted by noisy retrieved content and produces an unhelpful answer. Motivated by these issues, we introduce Semantic Uncertainty Guided Adaptive Retrieval (SUGAR), where we leverage context-based entropy to actively decide whether to retrieve and to further determine between single-step and multi-step retrieval. Our empirical results show that selective retrieval guided by semantic uncertainty estimation improves the performance across diverse question answering tasks, as well as achieves a more efficient inference.

本研究解决了大型语言模型（LLMs）在检索增强生成（RAG）过程中由于信息检索均一性导致的效能不足问题。提出的SUGAR方法基于上下文熵智能决策，使检索过程更具选择性并提高了多样化问答任务的表现，潜在地改善了模型的推理效率。

糖：利用上下文置信度实现更智能的检索