SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
This addresses inefficiencies in RAG systems for users relying on LLMs, though it is incremental as it builds on existing retrieval methods.
The paper tackled the problem of inefficient and noisy retrieval in retrieval-augmented generation (RAG) by introducing SUGAR, which uses semantic uncertainty to decide when and how to retrieve, improving performance and efficiency in question answering tasks.
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.