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RAG for Reducing LLM Hallucinations
How to read this map and check its sources? Illustrated guide
From 118 papers on this topic published since 2021, kept out of the 384 most relevant results of our searches on OpenAlex. 1 preprint counted once with its published version. Relevance was checked for the 120 closest papers only.
This is a sample of the field, not a census: a broad topic has many more papers than a map reads. Narrow the topic or edit the searches to map one part of it well.
The 4 searches run on OpenAlex · An overview of the field
- retrieval-augmented generation hallucination reduction
- grounded generation factuality evaluation
- knowledge retrieval factual question answering
- RAG verification and hallucination mitigation
The landscape
The retrieval-quality approach focuses on improving evidence selection and grounding within RAG, whereas the architecture approach examines the broader design, efficiency, and knowledge-integration choices of RAG systems. The combined-validation approach treats retrieval as one component of a wider hallucination-detection and mitigation toolkit, overlapping with RAG-specific work on confidence and verification. The task-specific approach brings these concerns to particular outputs, domains, and query settings, although its mostly title-only records provide limited detail about methods and results.
People
Ordered by the papers of the map they sign, counting more those they sign first, last or as corresponding author. Author order says who did what in many fields, not who leads a team; it is not used in alphabetical author lists, nor after the first fifteen authors of a large collaboration.
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Cheng Niu
Retrieval Quality and Grounding · 2
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Weihang Su
Hallucination Detection and Combined Validation · 1RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Yiqun Liu
Hallucination Detection and Combined Validation · 1RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Juntong Song
Retrieval Quality and Grounding · 2
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Aditya Deshpande
Retrieval Quality and Grounding · 1
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Akanksha Singh
Retrieval Quality and Grounding · 1
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Akshay Mittal
Hallucination Detection and Combined Validation · 1
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Akshitha Katkeri
Retrieval Quality and Grounding · 1
Their papers on the topic
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Ali Z. K. Matloob
Retrieval Quality and Grounding · 1
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Amit Kumar Chaudhary
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Anson Li
Retrieval Quality and Grounding · 1
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Apurva Narayan
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Arpana Nag
Hallucination Detection and Combined Validation · 1
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Ashish Jain
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Aske Plaat
Retrieval Quality and Grounding · 1
Their papers on the topic
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B. Lanitha
Retrieval Quality and Grounding · 1
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Babasaheb S. Satpute
Retrieval Quality and Grounding · 1
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Belkacem Mansouri
Hallucination Detection and Combined Validation · 1
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Bijoy Bhadra
Task-Specific Grounding and Detection · 1
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Bin Cui
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Caleb Barns
Retrieval Quality and Grounding · 1
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Chandan Kumar
Retrieval Quality and Grounding · 1
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Chandana Sree Mala
Retrieval Quality and Grounding · 1
Their papers on the topic
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Chi-Min Chan
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Chong Chen
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Chun Jason Xue
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Claire Dubois
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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D. Brindha
Hallucination Detection and Combined Validation · 1
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Danylo Yefimov
RAG Architectures and Knowledge Integration · 1
Their papers on the topic
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Debashis De
Task-Specific Grounding and Detection · 1
Institutions
These are publication affiliations, not identified research teams. A national organization such as CNRS can include several independent laboratories.
Ordered by the papers one of their authors signs first, last or as corresponding author, then by their share of the papers: a paper signed by four institutions counts a quarter for each, so a large collaboration does not make each of its members look central.
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Tsinghua University · CN
Hallucination Detection and Combined Validation · 1Retrieval Quality and Grounding · 1RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Weihang Su (2), Yiqun Liu (2), Yifan Feng (1), Yue Gao (1)
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Amazon (United States) · US
RAG Architectures and Knowledge Integration · 2Hallucination Detection and Combined Validation · 1Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Nicolò Cosimo Albanese (1), Tanay Chowdhury (1)
Matching papers with this affiliation
- Retrieval-Augmented Generation (RAG) for Large Language Models: A Comprehensive Survey
- In-Context Learning for Scalable and Online Hallucination Detection in RAGS
- Retrieval-Augmented Generation with Graphs (GraphRAG)
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
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Fudan University · CN
Retrieval Quality and Grounding · 1RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Fan Wu (1), Shan Xu (1), Xiaohua Wang (1)
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Tencent (China) · CN
RAG Architectures and Knowledge Integration · 2Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Hao Yu (1), Penghao Zhao (1), Shiwei Tong (1), Zhaofeng Liu (1)
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University of the Cumberlands · US
Hallucination Detection and Combined Validation · 1Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Aditya Deshpande (1), Akshay Mittal (1)
Matching papers with this affiliation
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Renmin University of China · CN
Retrieval Quality and Grounding · 2
Signing first, last or as corresponding author here: Ruiyang Ren (1), Zhongxiang Sun (1)
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Peking University · CN
Retrieval Quality and Grounding · 1RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Bin Cui (1), Penghao Zhao (1), Shenda Hong (1), Wentao Zhang (1)
Matching papers with this affiliation
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Huawei Technologies (China) · CN
Hallucination Detection and Combined Validation · 1RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Chong Chen (1), Lang Mei (1)
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Amnesty International Ireland · IE
Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Yashwant Singh (1)
Matching papers with this affiliation
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Amrita Vishwa Vidyapeetham · IN
Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Jasmin T J (1), Savitha Gopal (1)
Matching papers with this affiliation
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Ant Group (China) · CN
Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Feng Wang (1), Taoye Yin (1)
Matching papers with this affiliation
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Arizona State University · US
RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Deeksha Prahlad (1), Hokeun Kim (1)
Matching papers with this affiliation
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Bangladesh University of Engineering and Technology · BD
Task-Specific Grounding and Detection · 1
Signing first, last or as corresponding author here: Salman Rakin (1)
Matching papers with this affiliation
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Beijing University of Chinese Medicine · CN
Retrieval Quality and Grounding · 1
Signing first, last or as corresponding author here: Lina Ren (1)
Matching papers with this affiliation
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Cardiff University · GB
RAG Architectures and Knowledge Integration · 1
Signing first, last or as corresponding author here: Steven Schockaert (1)
Matching papers with this affiliation
Approaches
The papers are grouped by the similarity of their titles and abstracts; an AI names each group from its papers. A paper is in one group only.
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Retrieval Quality and Grounding
These papers review and develop RAG-specific hallucination mitigation through retrieval comparisons, retrieval optimization, confidence-aware retrieval, self-consistency verification, and handling evidence from multiple sources.
People: Cheng Niu, Juntong Song, Aditya Deshpande, Akanksha Singh, Akshitha Katkeri, Ali Z. K. Matloob
Institutions: Renmin University of China, Tsinghua University, Amazon (United States), Fudan University, Tencent (China)
Its papers
- Retrieval-Augmented Generation as an Epistemic Architecture: A Theoretical Account of Grounding, Provenance and Hallucination Reduction
- Evaluating Evidence Sensitivity and Robustness of Hallucination Detectors in Retrieval- Augmented Generation
- Hybrid retrieval–verification framework for hallucination reduction in medical LLMs
- Research on Factual Hallucination Detection and Mitigation in Large Language Models Based on Retrieval-Augmented Generation
- A Systematic Review of Hallucination Mitigation in Large Language Models Using Retrieval-Augmented Generation
- A Systematic Review of Hallucination Mitigation in Large Language Models Using Retrieval-Augmented Generation
- Why Retrieval Doesn't Cure Everything: A Review of Hallucination in Retrieval-Augmented Generation
- Comparative Analysis of Confidence-Aware and Verification Based Hallucination Reduction Techniques in Retrieval-Augmented Generation Systems
- Evaluating and Mitigating Hallucinations in Retrieval-Augmented Question Answering over Uzbek School Textbooks
- A multimodal cross-modal explainable retrieval-augmented generation framework for hallucination-grounded visual question answering
- A Systematic Review of Retrieval-Augmented Generation Techniques for Mitigating Hallucinations in Chatbot Systems
- LightweightRAG: A Statistically Validated, Training-Free Hybrid Retrieval-Augmented Generation Framework for Hallucination Reduction on CPU-Only Infrastructure
- A Hallucination-Resistant Document-Centric Conversational Framework Using Fusion Retrieval-Augmented Generation
- Detecting and Repairing Hallucinations in Retrieval-Augmented Generation
- Retrieval-Augmented Generation for Reducing Hallucinations in Drug-Related Question Answering Systems
- Quantum-Enhanced Retrieval-Augmented Generation for Hallucination Reduction in Large Language Models
- A Review of Hallucination Suppression Technologies for Large Language Models Under RAG Architecture
- Hallucination Reduction in Retrieval-Augmented Generation (RAG)
- Building a Reliable Retrieval-Augmented Generation (RAG) System with Hallucination Awareness
- Hallucination in Retrieval-Augmented Generation is a Context Construction Problem: A Constraint-Driven Cross-Modal Framework
- Hallucination in Large Language Models and Retrieval-Augmented Generation: Mechanisms, Mitigation, and Evaluation
- Reducing Hallucinations in Oncology Question Answering Using Confidence-Gated Retrieval-Augmented Generation
- A Multi-Source Knowledge Verification-Based Hallucination Mitigation Algorithm for Generative Writing Models
- The Double-Lock Framework: A Multi-Layered System for Grounded Retrieval-Augmented Generation and Hallucination Mitigation
- AMulti-Stage Hallucination Mitigation Framework for Reliable Retrieval-Augmented Generation Systems
- Reducing Hallucination in RAG Systems Through Confidence-Aware Retrieval and Self-Consistency Verification
- HAL-RAG: A Retrieval-Augmented Framework for Detecting and Mitigating Hallucinations in Large Language Model Responses
- Real-time Prevention of Factual Hallucinations in Retrieval-Augmented Generation
- Mitigating hallucinations in large language models through enhanced retrieval-augmented generation: A Framework with FAISS and Lang Chain
- Hyper-RAG: combating LLM hallucinations using hypergraph-driven retrieval-augmented generation
- Mitigating Hallucination in Financial Retrieval-Augmented Generation Via Fine-Grained Knowledge Verification
- SeaRAG: Reducing Hallucination in Retrieval-Augmented Generation via Statement-Entity Adaptive Ranking
- A Retrieval-Augmented Multi-Agent Framework for Reducing Hallucinations in Large Language Models for Research Assistance
- Reducing Hallucinations in Medical AI Through Citation Enforced Prompting in RAG Systems
- Enhancing Factual Reliability in Large Language Models Through Retrieval Augmented Generation
- A SYSTEMATIC REVIEW OF OPTIMIZED RETRIEVAL-AUGMENTED GENERATION FRAMEWORKS FOR REDUCING HALLUCINATIONS IN LARGE LANGUAGE MODELS
- Low-Hallucination RAG for Mathematical Question Answering: A Trustworthy Generation Method Based on Retrieval Hygiene, Step-Tree Reasoning, and Symbolic-Solver Feedback
- Retrieval-Augmented Generation (RAG) vs. Fine-Tuning: A Systematic Review of Hallucination Mitigation in Medical LLMs
- Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models
- Sufficiency-Gated Retrieval-Augmented Generation: Reducing Hallucination via Context Adequacy Estimation
- Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
- Reward-driven hallucination control in retrieval-augmented generation via bandit learning
- Scalable Retrieval-Augmented Generation for Technical Documents with Hallucination Risk Control
- Mitigating Artificial Intelligence Hallucinations in Education: A Comparative Study of Retrieval-Augmented Generation (RAG) and Large Language Models
- CAR-RAG: Category-Aware Hybrid Retrieval-Augmented Generation for Hallucination Mitigation
- Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
- Evaluating Retrieval-Augmented Generation Variants for Clinical Decision Support: Hallucination Mitigation and Secure On-Premises Deployment
- Attention Mapping for Hallucination Reduction in Arabic Financial Using Retrieval-Augmented Generation Systems
- MEGA-RAG: a retrieval-augmented generation framework with multi-evidence guided answer refinement for mitigating hallucinations of LLMs in public health
- Copy-Paste to Mitigate Large Language Model Hallucinations
- Retrieval-Augmented Generation and Hallucination in Large Language Models: A Scholarly Overview
- MultiRAG: A Knowledge-Guided Framework for Mitigating Hallucination in Multi-Source Retrieval Augmented Generation
- Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
- Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis
- Mitigating Hallucinations in RAG-Based AI Assistants Advanced Techniques and Mathematical Models for Reducing AI-Generated Misinformation
- ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
- Mitigating Hallucinations in Large Language Models: A Comparative Study of RAG-enhanced vs. Human-Generated Medical Templates
- Hallucination Reduction in Large Language Models with Retrieval-Augmented Generation Using Wikipedia Knowledge
- In-Context Learning for Scalable and Online Hallucination Detection in RAGS
- RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation
- RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
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RAG Architectures and Knowledge Integration
These papers survey RAG mechanisms and applications and examine knowledge-injection strategies, architectural variants, execution efficiency, and reliability when connecting language models to external information.
People: Weihang Su, Yiqun Liu, Amit Kumar Chaudhary, Apurva Narayan, Ashish Jain, Bin Cui
Institutions: Amazon (United States), Tencent (China), Tsinghua University, Fudan University, Peking University
Its papers
- The Impact of Retrieval Augmented Generation on the Reliability of Large Language Models
- Retrieval-Augmented Generation (RAG) Architectures: GraphRAG, Hybrid Reranking, and Context Drift Mitigation
- Impact of Retrieval‐Augmented Generation Framework for Natural Language Processing
- Retrieval-augmented generation for natural language processing: a survey
- EXPLORING RETRIEVAL-AUGMENTED GENERATION: MECHANISMS AND APPLICATIONS
- Lawsuit AraRAG: A Retrieval-Augmented Generation Framework for Arabic Legal Document Understanding and Hallucination Reduction
- A Survey on Retrieval-Augmented Text Generation for Large Language Models
- Retrieval-Augmented Generation (RAG) for Large Language Models: A Comprehensive Survey
- Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
- Retrieval-Augmented Generation for AI-Generated Content: A Survey
- Retrieval augmented generation for question-answering systems
- Context Denoising and Evidence Refinement for Retrieval Augmented Generation Models
- Retrieval-Augmented Generation for Question Answering and Beyond: A State-of-the-Art Review
- CONFRAG: Confidence-Guided Retrieval-Augmented Generation
- Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Question Answering Task
- Parametric Retrieval Augmented Generation
- Retrieval-Augmented Generation (RAG): Advances and Challenges
- SCMRAG: Self-Corrective Multihop Retrieval Augmented Generation System for LLM Agents
- Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
- A Survey of Multimodal Retrieval-Augmented Generation
- Accelerating Retrieval-Augmented Generation
- Evaluation of Retrieval-Augmented Generation: A Survey
- OG-RAG: Ontology-grounded retrieval-augmented generation for large language models
- KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
- Retrieval-Augmented Generation with Graphs (GraphRAG)
- A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
- RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
- Searching for Best Practices in Retrieval-Augmented Generation
- Ragas: Automated Evaluation of Retrieval Augmented Generation
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Hallucination Detection and Combined Validation
These papers review hallucination definitions and mitigation techniques, compare detection methods, and propose frameworks combining retrieval grounding with self-consistency, natural language inference, external verification, or revision.
People: Weihang Su, Yiqun Liu, Akshay Mittal, Arpana Nag, Belkacem Mansouri, D. Brindha
Institutions: Tsinghua University, Amazon (United States), University of the Cumberlands, Huawei Technologies (China)
Its papers
- Improving the Reliability of Large Language Models: A Hypothesis-Driven Statistical Evaluation of Hallucination Detection and Mitigation Techniques
- MORPHEUS v3.0 A Neuro-Symbolic Multi-Agent Architecture for Coherence Auditing and Hallucination Mitigation in Large Language Models
- Hallucination detection, verification, and correction in generative AI: A comprehensive survey
- A Hybrid Framework for Hallucination Detection & Mitigation in Large Language Models
- Hallucination Detection and Reduction in Large Language Models: A Practical Study
- A Multi-Stage Hallucination Reduction Framework for Large Language Models using Retrieval-Augmented Generation and NLI-based Verification
- Mitigating Hallucination in Large Language Models: Techniques, Applications, and Implications
- A Draft-Ground-Verify-Revise Framework for Reducing Hallucination in Large Language Models
- Dynamic Multi-view RAG: Mitigating Hallucinations of Large Language Models in Education
- A Unified Framework for Mitigating Hallucinations in Large Language Models: Integrating Internal and External Validation Strategies
- A Survey on Hallucination in Large Language Models: Definitions, Detection, and Mitigation
- Reducing Hallucinations in Large Language Models Through Integrated Self-Verification and Retrieval-Augmented Generation
- A comparative review of hallucination mitigation and performance improvement techniques in Small Language Models
- A Survey on Hallucination in Large Language and Foundation Models
- Mitigating Entity-Level Hallucination in Large Language Models
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
- Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
- Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
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Task-Specific Grounding and Detection
The supplied titles address RAG for product and technical-documentation question answering, domain adaptation, Indian code-mixed queries, multi-view summarization, structured outputs, and hallucination detection through layer-wise relevance propagation.
People: Bijoy Bhadra, Debashis De
Institutions: Bangladesh University of Engineering and Technology
Its papers
- An Answerability-Aware Retrieval-Augmented Generation Framework for Hallucination Reduction in Technical Documentation
- Reducing Hallucinations in Retrieval-Augmented LLM Question Answering for Indian Code-Mixed Queries
- Insight Lens: Multi-View Summarization via Retrieval-Augmented Generation for Hallucination Reduction
- Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction
- Lrp4rag: Detecting Hallucinations in Retrieval-Augmented Generation Via Layer-Wise Relevance Propagation
- Reducing LLM Hallucinations in Product Q&A Systems: A Retrieval-Augmented Generation Approach
- Reducing hallucination in structured outputs via Retrieval-Augmented Generation
Papers
- Improving the Reliability of Large Language Models: A Hypothesis-Driven Statistical Evaluation of Hallucination Detection and Mitigation Techniques
- Retrieval-Augmented Generation as an Epistemic Architecture: A Theoretical Account of Grounding, Provenance and Hallucination Reduction
- Evaluating Evidence Sensitivity and Robustness of Hallucination Detectors in Retrieval- Augmented Generation
- Hybrid retrieval–verification framework for hallucination reduction in medical LLMs
- Research on Factual Hallucination Detection and Mitigation in Large Language Models Based on Retrieval-Augmented Generation
- The Impact of Retrieval Augmented Generation on the Reliability of Large Language Models
- A Systematic Review of Hallucination Mitigation in Large Language Models Using Retrieval-Augmented Generation
- A Systematic Review of Hallucination Mitigation in Large Language Models Using Retrieval-Augmented Generation
- Retrieval-Augmented Generation (RAG) Architectures: GraphRAG, Hybrid Reranking, and Context Drift Mitigation
- MORPHEUS v3.0 A Neuro-Symbolic Multi-Agent Architecture for Coherence Auditing and Hallucination Mitigation in Large Language Models
- Why Retrieval Doesn't Cure Everything: A Review of Hallucination in Retrieval-Augmented Generation
- Comparative Analysis of Confidence-Aware and Verification Based Hallucination Reduction Techniques in Retrieval-Augmented Generation Systems
- Evaluating and Mitigating Hallucinations in Retrieval-Augmented Question Answering over Uzbek School Textbooks
- A multimodal cross-modal explainable retrieval-augmented generation framework for hallucination-grounded visual question answering
- A Systematic Review of Retrieval-Augmented Generation Techniques for Mitigating Hallucinations in Chatbot Systems
- LightweightRAG: A Statistically Validated, Training-Free Hybrid Retrieval-Augmented Generation Framework for Hallucination Reduction on CPU-Only Infrastructure
- A Hallucination-Resistant Document-Centric Conversational Framework Using Fusion Retrieval-Augmented Generation
- Detecting and Repairing Hallucinations in Retrieval-Augmented Generation
- Hallucination detection, verification, and correction in generative AI: A comprehensive survey
- Retrieval-Augmented Generation for Reducing Hallucinations in Drug-Related Question Answering Systems
- Quantum-Enhanced Retrieval-Augmented Generation for Hallucination Reduction in Large Language Models
- A Review of Hallucination Suppression Technologies for Large Language Models Under RAG Architecture
- Impact of Retrieval‐Augmented Generation Framework for Natural Language Processing
- A Hybrid Framework for Hallucination Detection & Mitigation in Large Language Models
- Hallucination Reduction in Retrieval-Augmented Generation (RAG)
- Building a Reliable Retrieval-Augmented Generation (RAG) System with Hallucination Awareness
- Hallucination Detection and Reduction in Large Language Models: A Practical Study
- Hallucination in Retrieval-Augmented Generation is a Context Construction Problem: A Constraint-Driven Cross-Modal Framework
- Hallucination in Large Language Models and Retrieval-Augmented Generation: Mechanisms, Mitigation, and Evaluation
- Reducing Hallucinations in Oncology Question Answering Using Confidence-Gated Retrieval-Augmented Generation
- Retrieval-augmented generation for natural language processing: a survey
- A Multi-Source Knowledge Verification-Based Hallucination Mitigation Algorithm for Generative Writing Models
- The Double-Lock Framework: A Multi-Layered System for Grounded Retrieval-Augmented Generation and Hallucination Mitigation
- AMulti-Stage Hallucination Mitigation Framework for Reliable Retrieval-Augmented Generation Systems
- Reducing Hallucination in RAG Systems Through Confidence-Aware Retrieval and Self-Consistency Verification
- HAL-RAG: A Retrieval-Augmented Framework for Detecting and Mitigating Hallucinations in Large Language Model Responses
- Real-time Prevention of Factual Hallucinations in Retrieval-Augmented Generation
- Mitigating hallucinations in large language models through enhanced retrieval-augmented generation: A Framework with FAISS and Lang Chain
- EXPLORING RETRIEVAL-AUGMENTED GENERATION: MECHANISMS AND APPLICATIONS
- Hyper-RAG: combating LLM hallucinations using hypergraph-driven retrieval-augmented generation
- Mitigating Hallucination in Financial Retrieval-Augmented Generation Via Fine-Grained Knowledge Verification
- Lawsuit AraRAG: A Retrieval-Augmented Generation Framework for Arabic Legal Document Understanding and Hallucination Reduction
- A Survey on Retrieval-Augmented Text Generation for Large Language Models
- SeaRAG: Reducing Hallucination in Retrieval-Augmented Generation via Statement-Entity Adaptive Ranking
- Retrieval-Augmented Generation (RAG) for Large Language Models: A Comprehensive Survey
- Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
- A Retrieval-Augmented Multi-Agent Framework for Reducing Hallucinations in Large Language Models for Research Assistance
- Reducing Hallucinations in Medical AI Through Citation Enforced Prompting in RAG Systems
- A Multi-Stage Hallucination Reduction Framework for Large Language Models using Retrieval-Augmented Generation and NLI-based Verification
- Enhancing Factual Reliability in Large Language Models Through Retrieval Augmented Generation
- A SYSTEMATIC REVIEW OF OPTIMIZED RETRIEVAL-AUGMENTED GENERATION FRAMEWORKS FOR REDUCING HALLUCINATIONS IN LARGE LANGUAGE MODELS
- Low-Hallucination RAG for Mathematical Question Answering: A Trustworthy Generation Method Based on Retrieval Hygiene, Step-Tree Reasoning, and Symbolic-Solver Feedback
- Mitigating Hallucination in Large Language Models: Techniques, Applications, and Implications
- Retrieval-Augmented Generation (RAG) vs. Fine-Tuning: A Systematic Review of Hallucination Mitigation in Medical LLMs
- Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models
- Retrieval-Augmented Generation for AI-Generated Content: A Survey
- A Draft-Ground-Verify-Revise Framework for Reducing Hallucination in Large Language Models
- Sufficiency-Gated Retrieval-Augmented Generation: Reducing Hallucination via Context Adequacy Estimation
- Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
- Dynamic Multi-view RAG: Mitigating Hallucinations of Large Language Models in Education
- An Answerability-Aware Retrieval-Augmented Generation Framework for Hallucination Reduction in Technical Documentation
- Reward-driven hallucination control in retrieval-augmented generation via bandit learning
- Retrieval augmented generation for question-answering systems
- Reducing Hallucinations in Retrieval-Augmented LLM Question Answering for Indian Code-Mixed Queries
- Context Denoising and Evidence Refinement for Retrieval Augmented Generation Models
- Retrieval-Augmented Generation for Question Answering and Beyond: A State-of-the-Art Review
- CONFRAG: Confidence-Guided Retrieval-Augmented Generation
- Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Question Answering Task
- Scalable Retrieval-Augmented Generation for Technical Documents with Hallucination Risk Control
- Mitigating Artificial Intelligence Hallucinations in Education: A Comparative Study of Retrieval-Augmented Generation (RAG) and Large Language Models
- Insight Lens: Multi-View Summarization via Retrieval-Augmented Generation for Hallucination Reduction
- CAR-RAG: Category-Aware Hybrid Retrieval-Augmented Generation for Hallucination Mitigation
- Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
- Evaluating Retrieval-Augmented Generation Variants for Clinical Decision Support: Hallucination Mitigation and Secure On-Premises Deployment
- Attention Mapping for Hallucination Reduction in Arabic Financial Using Retrieval-Augmented Generation Systems
- A Unified Framework for Mitigating Hallucinations in Large Language Models: Integrating Internal and External Validation Strategies
- MEGA-RAG: a retrieval-augmented generation framework with multi-evidence guided answer refinement for mitigating hallucinations of LLMs in public health
- A Survey on Hallucination in Large Language Models: Definitions, Detection, and Mitigation
- Copy-Paste to Mitigate Large Language Model Hallucinations
- Reducing Hallucinations in Large Language Models Through Integrated Self-Verification and Retrieval-Augmented Generation
- Parametric Retrieval Augmented Generation
- Retrieval-Augmented Generation (RAG): Advances and Challenges
- A comparative review of hallucination mitigation and performance improvement techniques in Small Language Models
- SCMRAG: Self-Corrective Multihop Retrieval Augmented Generation System for LLM Agents
- Retrieval-Augmented Generation and Hallucination in Large Language Models: A Scholarly Overview
- MultiRAG: A Knowledge-Guided Framework for Mitigating Hallucination in Multi-Source Retrieval Augmented Generation
- Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
- Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
- Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction
- A Survey on Hallucination in Large Language and Foundation Models
- A Survey of Multimodal Retrieval-Augmented Generation
- Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis
- Accelerating Retrieval-Augmented Generation
- Evaluation of Retrieval-Augmented Generation: A Survey
- Lrp4rag: Detecting Hallucinations in Retrieval-Augmented Generation Via Layer-Wise Relevance Propagation
- OG-RAG: Ontology-grounded retrieval-augmented generation for large language models
- Reducing LLM Hallucinations in Product Q&A Systems: A Retrieval-Augmented Generation Approach
- KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
- Mitigating Hallucinations in RAG-Based AI Assistants Advanced Techniques and Mathematical Models for Reducing AI-Generated Misinformation
- Retrieval-Augmented Generation with Graphs (GraphRAG)
- Mitigating Entity-Level Hallucination in Large Language Models
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
- ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
- Mitigating Hallucinations in Large Language Models: A Comparative Study of RAG-enhanced vs. Human-Generated Medical Templates
- THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
- Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
- A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
- Hallucination Reduction in Large Language Models with Retrieval-Augmented Generation Using Wikipedia Knowledge
- In-Context Learning for Scalable and Online Hallucination Detection in RAGS
- RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
- Reducing hallucination in structured outputs via Retrieval-Augmented Generation
- RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation
- RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
- Searching for Best Practices in Retrieval-Augmented Generation
- Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
- Ragas: Automated Evaluation of Retrieval Augmented Generation
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
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