SUGAR: Leveraging Contextual Confidence for Smarter Retrieval

• URL: http://arxiv.org/abs/2501.04899v1
• Date: Thu, 09 Jan 2025 01:24:59 GMT
• Title: SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
• Authors: Hanna Zubkova, Ji-Hoon Park, Seong-Whan Lee,
• Abstract summary: We introduce Semantic Uncertainty Guided Adaptive Retrieval (SUGAR) 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.
• Score: 28.552283701883766
• License:
• Abstract: 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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