用语义不确定性动态决定是否检索，提升问答效率与准确率
SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
- 基于上下文熵判断是否需要检索，避免无效调用
- 在多个问答任务中提升性能，推理效率提高30%以上
- 适合追求高效准确的LLM应用开发人员
考虑到大语言模型（LLMs）的参数知识有限，检索增强生成（RAG）通过引入外部知识来缓解幻觉问题。然而，统一检索支持性内容会导致响应生成源效率低下，因为并非所有情况都需要触发检索器，且当模型被噪声检索内容干扰时，可能生成无用回答。为此，我们提出语义不确定性引导的自适应检索（SUGAR），利用基于上下文的熵值主动决定是否检索，并进一步在单步与多步检索间选择。实验结果表明，由语义不确定性估计指导的选择性检索在多种问答任务中均提升了性能，同时实现了更高效的推理。
原文摘要 · Abstract (English)
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.
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