mlsanityData to 14 Oct 2026

RAC: Efficient LLM Factuality Correction with Retrieval Augmentation

arXiv 2410.15667 ↗First posted 21 Oct 2024cs.CL cs.AI cs.LGcited by 5

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

Abstract (arXiv)

Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This paper introduces a simple but effective low-latency post-correction method, Retrieval Augmented Correction (RAC), aimed at enhancing the factual performance of LLMs without requiring additional fine-tuning. Our method is general and can be used with any instruction-tuned LLM, and has greatly reduced latency compared to prior approaches. RAC decomposes the LLM's output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the LLM-generated output. Our extensive experiments show that RAC yields up to 30% improvements over state-of-the-art baselines across two popular factuality evaluation datasets, validating its efficacy and robustness in both with and without the integration of Retrieval-Augmented Generation (RAG) across different LLMs.footnoteOur code is at urlhttps://github.com/jlab-nlp/Retrieval-Augmented-Correction

Key facts. Cited by 5 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 25 of its 39 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

25 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  7. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  8. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  9. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  11. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  12. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
12 more
  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  2. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  3. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  4. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  5. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  6. 2023Fine-tuning Language Models for Factualitycited by 221
  7. 2022Factuality Enhanced Language Models for Open-Ended Text Generationcited by 202
  8. 2022DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answeringcited by 96
  9. 2024Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluationcited by 80
  10. 2024Reliable, Adaptable, and Attributable Language Models with Retrievalcited by 73
  11. 2023Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectificationcited by 38
  12. 2024Fine-Grained Self-Endorsement Improves Factuality and Reasoningcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 26
  2. 2024Improving Factuality with Explicit Working Memorycited by 10
  3. 2025Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verificationcited by 2
  4. 2025Mathematical Analysis of Hallucination Dynamics in Large Language Models: Uncertainty Quantification, Advanced Decoding, and Principled Mitigationcited by 0
  5. 2026ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output
  2. 2026SERC: LDPC-Inspired Semantic Error Correction for Retrieval-Augmented Generation
  3. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
  4. 2026Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights
  5. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
  6. 2024Long-form factuality in large language models
  7. 2025Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
  8. 2024FlashBack: Efficient Retrieval-Augmented Language Modeling for Fast Inference