mlsanityData to 7 Oct 2026

Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output

arXiv 2411.01022 ↗First posted 1 Nov 2024cs.CLcited by 4

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

Abstract (arXiv)

We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG). Given a context and putative output, we compute a factuality score that can be thresholded to yield a binary decision to check the results of LLM-based question-answering, summarization, or other systems. Unlike factuality checkers that themselves rely on LLMs, we use compact, open-source natural language inference (NLI) models that yield a freely accessible solution with low latency and low cost at run-time, and no need for LLM fine-tuning. The approach also enables downstream mitigation and correction of hallucinations, by tracing them back to specific context chunks. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets, indicating the effectiveness of our method for fact-checking RAG output.

Key facts. Cited by 4 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 12 of its 18 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

12 of 18 references.

References · 12

  1. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  2. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  4. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  5. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  6. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  7. 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
  8. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  9. 2023Do Language Models Know When They're Hallucinating References?cited by 115
  10. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 55
  11. 2023Evaluating and Modeling Attribution for Cross-Lingual Question Answeringcited by 21
  12. 2017Neural Models for Sequence Chunkingcited by 10

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2025Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluationcited by 20
  2. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Modelscited by 13
  3. 2025FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domaincited by 1
  4. 2026Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation
  2. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
  3. 2025FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data
  4. 2026Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
  5. 2024Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
  6. 2024Long-form factuality in large language models
  7. 2026Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
  8. 2025Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction