# FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge

arXiv: https://arxiv.org/abs/2310.17119 · HTML: https://mlsanity.com/paper/2310.17119/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [Reasoning Over Semantic-Level Graph for Fact Checking](https://mlsanity.com/paper/1909.03745/) (2019)
- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [Paragraph-based Transformer Pre-training for Multi-Sentence Inference](https://mlsanity.com/paper/2205.01228/) (2022)

## Built on by (most cited first, 25 of 25)

- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [Building Guardrails for Large Language Models](https://mlsanity.com/paper/2402.01822/) (2024)
- [Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective](https://mlsanity.com/paper/2411.14258/) (2024)
- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [Learning to Refine with Fine-Grained Natural Language Feedback](https://mlsanity.com/paper/2407.02397/) (2024)
- [Mitigating Large Language Model Hallucination with Faithful Finetuning](https://mlsanity.com/paper/2406.11267/) (2024)
- [Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval](https://mlsanity.com/paper/2405.06545/) (2024)
- [FACTOID: FACtual enTailment fOr hallucInation Detection](https://mlsanity.com/paper/2403.19113/) (2024)
- [FactLens: Benchmarking Fine-Grained Fact Verification](https://mlsanity.com/paper/2411.05980/) (2024)
- [Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling](https://mlsanity.com/paper/2409.11283/) (2024)
- [Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression](https://mlsanity.com/paper/2405.00301/) (2024)
- [A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models](https://mlsanity.com/paper/2507.05288/) (2025)
- [Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion](https://mlsanity.com/paper/2410.10408/) (2024)
- [The Narrative Continuity Test: A Conceptual Framework for Evaluating Identity Persistence in AI Systems](https://mlsanity.com/paper/2510.24831/) (2025)
- [Better Datasets Start From RefineLab: Automatic Optimization for High-Quality Dataset Refinement](https://mlsanity.com/paper/2511.06530/) (2025)
- [PASS-FC: Progressive and Adaptive Search Scheme for Fact Checking of Comprehensive Claims](https://mlsanity.com/paper/2504.09866/) (2025)
- [MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations](https://mlsanity.com/paper/2505.14101/) (2025)
- [Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling](https://mlsanity.com/paper/2506.00064/) (2025)
- [VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference](https://mlsanity.com/paper/2410.09455/) (2024)
- [UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge](https://mlsanity.com/paper/2502.13648/) (2025)
- [ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos](https://mlsanity.com/paper/2509.20467/) (2025)
- [JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation](https://mlsanity.com/paper/2510.19310/) (2025)
- [Mask-to-Correct^+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction](https://mlsanity.com/paper/2605.18776/) (2026)
- [An Empirical Analysis of Factual Errors in Human-Written Text and Its Application to Factual Error Detection](https://mlsanity.com/paper/2606.27959/) (2026)
- [Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference](https://mlsanity.com/paper/2610.20292/) (2026)

## Abstract (arXiv)

Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to attribute their claims to external knowledge and their tendency to hallucinate makes it difficult to rely on their responses. Humans, too, are prone to factual errors in their writing. Since manual detection and correction of factual errors is labor-intensive, developing an automatic approach can greatly reduce human effort. We present FLEEK, a prototype tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors using the collected evidence. Initial empirical evaluation on fact error detection (77-85% F1) shows the potential of FLEEK. A video demo of FLEEK can be found at https://youtu.be/NapJFUlkPdQ.