# FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs

arXiv: https://arxiv.org/abs/2503.17229 · HTML: https://mlsanity.com/paper/2503.17229/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs](https://mlsanity.com/paper/2406.15927/) (2024)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning](https://mlsanity.com/paper/2308.00436/) (2023)
- [Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://mlsanity.com/paper/2407.07071/) (2024)
- [CriticBench: Benchmarking LLMs for Critique-Correct Reasoning](https://mlsanity.com/paper/2402.14809/) (2024)
- [Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://mlsanity.com/paper/2402.09267/) (2024)
- [LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints](https://mlsanity.com/paper/2410.06458/) (2024)
- [GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework](https://mlsanity.com/paper/2407.10793/) (2024)
- [Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling](https://mlsanity.com/paper/2409.11283/) (2024)

## Built on by (most cited first, 14 of 14)

- [HALT: Hallucination Assessment via Log-probs as Time series](https://mlsanity.com/paper/2602.02888/) (2026)
- [Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction](https://mlsanity.com/paper/2605.05134/) (2026)
- [Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMs](https://mlsanity.com/paper/2508.19366/) (2025)
- [Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation](https://mlsanity.com/paper/2512.16189/) (2025)
- [Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses](https://mlsanity.com/paper/2602.01285/) (2026)
- [HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models](https://mlsanity.com/paper/2604.26139/) (2026)
- [TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection](https://mlsanity.com/paper/2606.01033/) (2026)
- [JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation](https://mlsanity.com/paper/2510.19310/) (2025)
- [Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2604.10697/) (2026)
- [Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement](https://mlsanity.com/paper/2605.05103/) (2026)
- [KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering](https://mlsanity.com/paper/2606.00328/) (2026)
- [A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy](https://mlsanity.com/paper/2606.24115/) (2026)
- [Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics](https://mlsanity.com/paper/2608.19579/) (2026)
- [Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems](https://mlsanity.com/paper/2609.25945/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination detection methods typically operate at the sentence level or passage level, we propose FactSelfCheck, a novel zero-resource black-box sampling-based method that enables fine-grained fact-level detection. Our approach represents text as interpretable knowledge graphs consisting of facts in the form of triples, providing clearer insights into content factuality than traditional approaches. Through analyzing factual consistency across multiple LLM responses, we compute fine-grained hallucination scores without requiring external resources or training data. Our evaluation demonstrates that FactSelfCheck performs competitively with leading sentence-level sampling-based methods while providing more detailed and interpretable insights. Most notably, our fact-level approach significantly improves hallucination correction, achieving a 35.5% increase in factual content compared to the baseline, while sentence-level SelfCheckGPT yields only a 10.6% improvement. The granular nature of our detection enables more precise identification and correction of hallucinated content. Additionally, we contribute FavaMultiSamples, a novel dataset that addresses a gap in the field by providing the research community with a second dataset for evaluating sampling-based methods.