mlsanityData to 14 Oct 2026

RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

arXiv 2402.16457 ↗First posted 26 Feb 2024cs.CLcited by 13

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

Abstract (arXiv)

Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency and relevance of the sourced information. However, previous works largely overlook the evaluation of ARAG approaches, leading to their effectiveness being understudied. This work presents a benchmark, RetrievalQA, comprising 1,271 short-form questions covering new world and long-tail knowledge. The knowledge necessary to answer the questions is absent from LLMs; therefore, external information must be retrieved to answer correctly. This makes RetrievalQA a suitable testbed to evaluate existing ARAG methods. We observe that calibration-based methods heavily rely on threshold tuning, while vanilla prompting is inadequate for guiding LLMs to make reliable retrieval decisions. Based on our findings, we propose Time-Aware Adaptive Retrieval (TA-ARE), a simple yet effective method that helps LLMs assess the necessity of retrieval without calibration or additional training. The dataset and code will be available at https://github.com/hyintell/RetrievalQA

Key facts. Cited by 13 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 31 of its 36 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

31 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct
  2. 2023Benchmarking Large Language Models in Retrieval-Augmented Generation4 Sep

Other references · 29

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  8. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2019Language Models as Knowledge Bases?cited by 1,936
  11. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
17 more
  1. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  2. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  3. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  4. 2024TinyLlama: An Open-Source Small Language Modelcited by 618
  5. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 565
  6. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  7. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  8. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  9. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  10. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 318
  11. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 274
  12. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  13. 2022RealTime QA: What's the Answer Right Now?cited by 244
  14. 2023Do Large Language Models Know What They Don't Know?cited by 243
  15. 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 147
  16. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 122
  17. 2023Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Modelscited by 46

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 2.

13 papers.

Sort:

By area:

  1. 2024Certifiably Robust RAG against Retrieval Corruptioncited by 151
  2. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 113
  3. 2024Privacy-Preserving Retrieval-Augmented Generation with Differential Privacycited by 39
  4. 2025LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routingcited by 29
  5. 2025UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularitiescited by 20
  6. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  7. 2025LPFQA: A Long-Tail Professional Forum-based Benchmark for LLM Evaluationcited by 4
  8. 2025CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineeringcited by 3
  9. 2025What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Modelscited by 1
  10. 2025Iterative Tree Analysis for Medical Criticscited by 0
  11. 2025References Indeed Matter? Reference-Free Preference Optimization for Conversational Query Reformulationcited by 0
  12. 2026Before Answering: Evidence Sufficiency under Size-Matched Memory Constructioncited by 0
1 more
  1. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
  2. 2025Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?
  3. 2025Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k
  4. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
  5. 2024Context Awareness Gate For Retrieval Augmented Generation
  6. 2025Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation
  7. 2024Efficient In-Domain Question Answering for Resource-Constrained Environments
  8. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering