# HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

arXiv: https://arxiv.org/abs/2504.07069 · HTML: https://mlsanity.com/paper/2504.07069/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://mlsanity.com/paper/2012.00955/) (2020)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge](https://mlsanity.com/paper/2410.02736/) (2024)
- [RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://mlsanity.com/paper/2401.00396/) (2023)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [Factuality Enhanced Language Models for Open-Ended Text Generation](https://mlsanity.com/paper/2206.04624/) (2022)
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [Fine-grained Hallucination Detection and Editing for Language Models](https://mlsanity.com/paper/2401.06855/) (2024)
- [HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection](https://mlsanity.com/paper/2409.17504/) (2024)
- [CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models](https://mlsanity.com/paper/2405.13684/) (2024)
- [Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models](https://mlsanity.com/paper/2409.02976/) (2024)
- [Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses](https://mlsanity.com/paper/2407.05474/) (2024)

## Built on by (most cited first, 13 of 13)

- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.07937/) (2026)
- [CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection](https://mlsanity.com/paper/2506.05243/) (2025)
- [Collective Hallucination in Multi-Agent LLMs:Modeling and Defense](https://mlsanity.com/paper/2606.07941/) (2026)
- [PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations](https://mlsanity.com/paper/2604.16909/) (2026)
- [Confabulations from ACL Publications (CAP): A Dataset for Scientific Hallucination Detection](https://mlsanity.com/paper/2510.22395/) (2025)
- [Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2511.17946/) (2025)
- [CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models](https://mlsanity.com/paper/2602.00085/) (2026)
- [RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration](https://mlsanity.com/paper/2604.15945/) (2026)
- [Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction](https://mlsanity.com/paper/2605.27706/) (2026)
- [AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification](https://mlsanity.com/paper/2601.03605/) (2026)
- [Graph Alignment Topology as an Inductive Bias for Grounding Detection](https://mlsanity.com/paper/2605.22963/) (2026)
- [Empirical Analysis and Detection of Hallucinations in LLM-Generated Bug Report Summaries](https://mlsanity.com/paper/2605.24137/) (2026)

## Abstract (arXiv)

This paper introduces a comprehensive system for detecting hallucinations in large language model (LLM) outputs in enterprise settings. We present a novel taxonomy of LLM responses specific to hallucination in enterprise applications, categorizing them into context-based, common knowledge, enterprise-specific, and innocuous statements. Our hallucination detection model HDM-2 validates LLM responses with respect to both context and generally known facts (common knowledge). It provides both hallucination scores and word-level annotations, enabling precise identification of problematic content. To evaluate it on context-based and common-knowledge hallucinations, we introduce a new dataset HDMBench. Experimental results demonstrate that HDM-2 out-performs existing approaches across RagTruth, TruthfulQA, and HDMBench datasets. This work addresses the specific challenges of enterprise deployment, including computational efficiency, domain specialization, and fine-grained error identification. Our evaluation dataset, model weights, and inference code are publicly available.