mlsanityData to 7 Oct 2026

Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks

arXiv 2305.17653 ↗First posted 28 May 2023cs.CLcited by 21

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

Abstract (arXiv)

Retrieval-augmented methods have received increasing attention to support downstream tasks by leveraging useful information from external resources. Recent studies mainly focus on exploring retrieval to solve knowledge-intensive (KI) tasks. However, the potential of retrieval for most non-knowledge-intensive (NKI) tasks remains under-explored. There are two main challenges to leveraging retrieval-augmented methods for NKI tasks: 1) the demand for diverse relevance score functions and 2) the dilemma between training cost and task performance. To address these challenges, we propose a two-stage framework for NKI tasks, named PGRA. In the first stage, we adopt a task-agnostic retriever to build a shared static index and select candidate evidence efficiently. In the second stage, we design a prompt-guided reranker to rerank the nearest evidence according to task-specific relevance for the reader. Experimental results show that PGRA outperforms other state-of-the-art retrieval-augmented methods. Our analyses further investigate the influence factors to model performance and demonstrate the generality of PGRA. Codes are available at https://github.com/THUNLP-MT/PGRA.

Key facts. Cited by 21 papers 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 31 of its 43 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

31 of 43 references.

References · 31

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  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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19 more
  1. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  2. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 8; 2026: 2.

21 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  3. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 119
  4. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 70
  5. 2023Self-Knowledge Guided Retrieval Augmentation for Large Language Modelscited by 69
  6. 2024BioRAG: A RAG-LLM Framework for Biological Question Reasoningcited by 42
  7. 2025MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation Systemcited by 19
  8. 2024Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learningcited by 13
  9. 2024DEEM: Dynamic Experienced Expert Modeling for Stance Detectioncited by 12
  10. 2024Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Studycited by 11
  11. 2024ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusioncited by 5
  12. 2025IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generationcited by 4
9 more
  1. 2025GPR: Empowering Generation with Graph-Pretrained Retrievercited by 3
  2. 2025Considering Length Diversity in Retrieval-Augmented Summarizationcited by 2
  3. 2025Evaluating the Retrieval Robustness of Large Language Modelscited by 2
  4. 2025A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunitiescited by 2
  5. 2024Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledgecited by 1
  6. 2025Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generationcited by 0
  7. 2025SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offscited by 0
  8. 2026KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrievalcited by 0
  9. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

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  1. 2025RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation
  2. 2025Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion
  3. 2026A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
  4. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
  5. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
  6. 2025ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses
  7. 2025ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning
  8. 2025LimRank: Less is More for Reasoning-Intensive Information Reranking