mlsanityData to 14 Oct 2026

Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control

arXiv 2502.12145 ↗First posted 17 Feb 2025cs.IR cs.AIcited by 6

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to mitigate large language model (LLM) hallucinations by incorporating external knowledge retrieval. However, existing RAG frameworks often apply retrieval indiscriminately,leading to inefficiencies-over-retrieving when unnecessary or failing to retrieve iteratively when required for complex reasoning. Recent adaptive retrieval strategies, though adaptively navigates these retrieval strategies, predict only based on query complexity and lacks user-driven flexibility, making them infeasible for diverse user application needs. In this paper, we introduce a novel user-controllable RAG framework that enables dynamic adjustment of the accuracy-cost trade-off. Our approach leverages two classifiers: one trained to prioritize accuracy and another to prioritize retrieval efficiency. Via an interpretable control parameter α, users can seamlessly navigate between minimal-cost retrieval and high-accuracy retrieval based on their specific requirements. We empirically demonstrate that our approach effectively balances accuracy, retrieval cost, and user controllability, making it a practical and adaptable solution for real-world applications. Code is available at https://github.com/JinyanSu1/Flare-Aug.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 34 of its 37 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

34 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval17 Oct
  2. 2024LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation22 Apr
  3. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models15 Mar

Other references · 31

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  10. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  11. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  12. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
19 more
  1. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  3. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  4. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 800
  5. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  6. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 682
  7. 2023Active Retrieval Augmented Generationcited by 516
  8. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  9. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  10. 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 111
  11. 2023Self-Knowledge Guided Retrieval Augmentation for Large Language Modelscited by 69
  12. 2024Learning to Route LLMs with Confidence Tokenscited by 67
  13. 2024TensorOpera Router: A Multi-Model Router for Efficient LLM Inferencecited by 60
  14. 2025MixLLM: Dynamic Routing in Mixed Large Language Modelscited by 59
  15. 2024SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generationcited by 40
  16. 2024Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanationscited by 30
  17. 2024MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexitycited by 29
  18. 2024Adaptive Retrieval-Augmented Generation for Conversational Systemscited by 22
  19. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attackscited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  2. 2026From Lossy to Verified: A Provenance-Aware Tiered Memory for Agentscited by 17
  3. 2026RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routingcited by 6
  4. 2026On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experimentcited by 2
  5. 2026GAM-RAG: Gain-Adaptive Memory for Evolving Retrieval in Retrieval-Augmented Generationcited by 1
  6. 2025Harmonia: End-to-End RAG Serving Optimizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  2. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  3. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  4. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
  5. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  6. 2025Dynamic and Parametric Retrieval-Augmented Generation
  7. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  8. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions