mlsanityData to 14 Oct 2026

Uncertainty Quantification in Retrieval Augmented Question Answering

arXiv 2502.18108 ↗First posted 25 Feb 2025cs.CLcited by 8

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval augmented Question Answering (QA) helps QA models overcome knowledge gaps by incorporating retrieved evidence, typically a set of passages, alongside the question at test time. Previous studies show that this approach improves QA performance and reduces hallucinations, without, however, assessing whether the retrieved passages are indeed useful at answering correctly. In this work, we propose to quantify the uncertainty of a QA model via estimating the utility of the passages it is provided with. We train a lightweight neural model to predict passage utility for a target QA model and show that while simple information theoretic metrics can predict answer correctness up to a certain extent, our approach efficiently approximates or outperforms more expensive sampling-based methods. Code and data are available at https://github.com/lauhaide/ragu.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 71 of its 99 references that are in the map. First posted on arXiv 25 Feb 2025.

Builds on

71 of 99 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systems9 Nov
  2. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity21 Mar
  3. 2024REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering27 Feb
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 67

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
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  12. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
38 more
  1. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
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  17. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
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50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answeringcited by 7
  2. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 6
  3. 2025Modeling Contextual Passage Utility for Multihop Question Answeringcited by 2
  4. 2026INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimationcited by 2
  5. 2026Parallel Context-of-Experts Decoding for Retrieval Augmented Generationcited by 1
  6. 2026Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Predictioncited by 1
  7. 2026Can LLMs Take Retrieved Information with a Grain of Salt?cited by 0
  8. 2026Uncertainty Quantification for Multimodal Retrieval Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Interpretable Uncertainty for Adaptive Retrieval and Reasoning in Question Answering
  2. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
  3. 2026URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
  4. 2025To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation
  5. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering
  6. 2026Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
  7. 2024MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
  8. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering