mlsanityData to 7 Oct 2026

Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

arXiv 2403.10446 ↗First posted 15 Mar 2024cs.CL cs.LGcited by 76

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

Abstract (arXiv)

We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases. Our system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. Addressing the challenge of LLM hallucinations, we finetune models with a curated dataset which originates from CMU's extensive resources and annotated with the teacher model. Our experiments demonstrate the system's effectiveness in generating more accurate answers to domain-specific and time-sensitive inquiries. The results also revealed the limitations of fine-tuning LLMs with small-scale and skewed datasets. This research highlights the potential of RAG systems in augmenting LLMs with external datasets for improved performance in knowledge-intensive tasks. Our code and models are available on Github.

Key facts. Cited by 76 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 9 of its 16 references that are in the map. First posted on arXiv 15 Mar 2024.

Builds on

9 of 16 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  4. 2023GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointscited by 1,311
  5. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  6. 2023StarCoder: may the source be with you!cited by 790
  7. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  8. 2022A Survey on In-context Learningcited by 717
  9. 2023Larger language models do in-context learning differentlycited by 386

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 19; 2025: 46; 2026: 11.

76 papers.

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  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  2. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  3. 2024Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrievalcited by 45
  4. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  5. 2024Logically Consistent Language Models via Neuro-Symbolic Integrationcited by 30
  6. 2024DataGen: Unified Synthetic Dataset Generation via Large Language Modelscited by 22
  7. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practicescited by 22
  8. 2025DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMscited by 21
  9. 2024C-FedRAG: A Confidential Federated Retrieval-Augmented Generation Systemcited by 19
  10. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challengescited by 19
  11. 2024Do Large Language Models Need a Content Delivery Network?cited by 15
  12. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attackscited by 14
38 more
  1. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 14
  2. 2025Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoningcited by 13
  3. 2025Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAGcited by 13
  4. 2024Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generationcited by 12
  5. 2024Graph-based Uncertainty Metrics for Long-form Language Model Outputscited by 11
  6. 2024Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarizationcited by 9
  7. 2025A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysiscited by 9
  8. 2025InfoGain-RAG: Boosting Retrieval-Augmented Generation via Document Information Gain-based Reranking and Filteringcited by 8
  9. 2025Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAGcited by 8
  10. 2024Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucinationcited by 7
  11. 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systemscited by 7
  12. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systemscited by 7
  13. 2024LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answeringcited by 6
  14. 2024Reliable Decision Making via Calibration Oriented Retrieval Augmented Generationcited by 6
  15. 2025Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systemscited by 6
  16. 2025RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QAcited by 6
  17. 2025Towards Geo-Culturally Grounded LLM Generationscited by 5
  18. 2025A Survey on Proactive Defense Strategies Against Misinformation in Large Language Modelscited by 5
  19. 2025DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injectioncited by 5
  20. 2025From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMscited by 5
  21. 2025Automated Formalization via Conceptual Retrieval-Augmented LLMscited by 5
  22. 2024Self-adaptive Multimodal Retrieval-Augmented Generationcited by 4
  23. 2025FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGcited by 4
  24. 2025Optimizing Retrieval Augmented Generation for Object Constraint Languagecited by 4
  25. 2026DTCRS: Dynamic Tree Construction for Recursive Summarizationcited by 4
  26. 2024Hybrid Querying Over Relational Databases and Large Language Modelscited by 3
  27. 2025What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysiscited by 2
  28. 2025An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answeringcited by 2
  29. 2025ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosiscited by 2
  30. 2025Can we Evaluate RAGs with Synthetic Data?cited by 2
  31. 2025Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generationcited by 2
  32. 2025CFVBench: A Comprehensive Video Benchmark for Fine-grained Multimodal Retrieval-Augmented Generationcited by 2
  33. 2025Roles of MLLMs in Visually Rich Document Retrieval for RAG: A Surveycited by 2
  34. 2025Socio-technical aspects of Agentic AIcited by 2
  35. 2026Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Modelscited by 2
  36. 2026IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Reviewcited by 2
  37. 2024LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAGcited by 1
  38. 2024Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devicescited by 1

50 most cited of 76 All 76 (JSON)

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  1. 2025Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
  2. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  4. 2025DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
  5. 2025Tuning LLMs by RAG Principles: Towards LLM-native Memory
  6. 2025Know Or Not: a library for evaluating out-of-knowledge base robustness
  7. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  8. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation