mlsanityData to 14 Oct 2026

DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation

arXiv 2504.10198 ↗First posted 14 Apr 2025cs.CLcited by 7

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

Abstract (arXiv)

Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG methods face significant limitations in two key aspects: 1) Lack of an effective mechanism to control retrieval triggers, and 2) Lack of effective scrutiny of retrieval content. To address these limitations, we propose an innovative dynamic RAG method, DioR (Adaptive Cognitive Detection and Contextual Retrieval Optimization), which consists of two main components: adaptive cognitive detection and contextual retrieval optimization, specifically designed to determine when retrieval is needed and what to retrieve for LLMs is useful. Experimental results demonstrate that DioR achieves superior performance on all tasks, demonstrating the effectiveness of our work.

Key facts. Cited by 7 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 33 of its 48 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

33 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs21 Jun

Other references · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  8. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  9. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  11. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  12. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
20 more
  1. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  2. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  3. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  4. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  5. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  6. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 93
  7. 2024Understanding the Weakness of Large Language Model Agents within a Complex Android Environmentcited by 60
  8. 2020SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Modelscited by 56
  9. 2023On Early Detection of Hallucinations in Factual Question Answeringcited by 56
  10. 2022Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformercited by 45
  11. 2024A Survey on Large Language Model Hallucination via a Creativity Perspectivecited by 38
  12. 2024Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trendscited by 25
  13. 2024Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Checkcited by 24
  14. 2020IIRC: Incremental Implicitly-Refined Classificationcited by 20
  15. 2024CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answeringcited by 17
  16. 2024Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answeringcited by 13
  17. 2023KNN-LM Does Not Improve Open-ended Text Generationcited by 10
  18. 2024Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topicscited by 9
  19. 2024Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QAcited by 9
  20. 2024Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systemscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Storescited by 3
  2. 2025Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generationcited by 2
  3. 2026CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answeringcited by 1
  4. 2026SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognitioncited by 1
  5. 2026FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skillscited by 1
  6. 2025MuPlon: Multi-Path Causal Optimization for Claim Verification through Controlling Confoundingcited by 0
  7. 2026Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Dynamic Context Selection for Retrieval-Augmented Generation: Mitigating Distractors and Positional Bias
  2. 2024Searching for Best Practices in Retrieval-Augmented Generation
  3. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
  4. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  5. 2025RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance
  6. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  7. 2025Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
  8. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control