SUGAR: Leveraging Contextual Confidence for Smarter Retrieval
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912181851783168 |
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| author | Zubkova, Hanna Park, Ji-Hoon Lee, Seong-Whan |
| author_facet | Zubkova, Hanna Park, Ji-Hoon Lee, Seong-Whan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_04899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SUGAR: Leveraging Contextual Confidence for Smarter Retrieval Zubkova, Hanna Park, Ji-Hoon Lee, Seong-Whan Computation and Language Artificial Intelligence 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. |
| title | SUGAR: Leveraging Contextual Confidence for Smarter Retrieval |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.04899 |