mlsanityData to 7 Oct 2026

Predicting the Benefit of Retrieval Augmentation in Open-Domain Question Answering

arXiv 2604.07985 ↗First posted 9 Apr 2026cs.CL cs.IRcited by 1

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

Abstract (arXiv)

While retrieval augmented generation has become a common approach for enhancing question answering systems, retrieval is not universally advantageous. We study the problem of predicting whether incorporating external retrieved information is likely to improve response quality for a given question. To this end, we evaluate a range of prediction methods that are based on retrieval signals, answer characteristics, and semantic consistency between generated responses and retrieved passages. We further devise a predictor that probes the LLM's internal state. Its prediction performance significantly narrows the performance gap between post-generation methods which are computationally demanding and pre-generation (post-retrieval) methods. We use the prediction methods to devise a selective retrieval framework that dynamically chooses between retrieval and non-retrieval generation modes per question. Experimental results demonstrate that selectively applying retrieval augmentation yields answer quality that transcends that of using retrieval for all queries.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 49 references that are in the map. First posted on arXiv 9 Apr 2026.

Builds on

30 of 49 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation20 Jan
  2. 2025The Distracting Effect: Understanding Irrelevant Passages in RAG11 May
  3. 2025SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction3 Mar
  4. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity21 Mar
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 25

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  8. 2017Billion-scale similarity search with GPUscited by 2,477
  9. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  11. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  12. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
13 more
  1. 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 527
  2. 2023Active Retrieval Augmented Generationcited by 506
  3. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  4. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  5. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
  6. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  7. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
  8. 2024DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Modelscited by 34
  9. 2024Adaptive Retrieval-Augmented Generation for Conversational Systemscited by 22
  10. 2023Building Efficient Universal Classifiers with Natural Language Inferencecited by 19
  11. 2025Is Relevance Propagated from Retriever to Generator in RAG?cited by 11
  12. 2025Probing the Difficulty Perception Mechanism of Large Language Modelscited by 8
  13. 2025Revisiting NLI: Towards Cost-Effective and Human-Aligned Metrics for Evaluating LLMs in Question Answeringcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On-Demand Attention: Language Models Know When to Recallcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
  2. 2024RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
  3. 2023Understanding Retrieval Augmentation for Long-Form Question Answering
  4. 2026Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation
  5. 2026A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation
  6. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
  7. 2024Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis
  8. 2025Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation