# InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers

arXiv: https://arxiv.org/abs/2403.02889 · HTML: https://mlsanity.com/paper/2403.02889/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (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)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization](https://mlsanity.com/paper/2301.12307/) (2023)

## Built on by (most cited first, 27 of 27)

- [Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art](https://mlsanity.com/paper/2403.16527/) (2024)
- [Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2501.09997/) (2025)
- [DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning](https://mlsanity.com/paper/2603.16459/) (2026)
- [Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation](https://mlsanity.com/paper/2503.16789/) (2025)
- [A Survey of Automatic Hallucination Evaluation on Natural Language Generation](https://mlsanity.com/paper/2404.12041/) (2024)
- [Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts](https://mlsanity.com/paper/2511.12236/) (2025)
- [SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion](https://mlsanity.com/paper/2505.07528/) (2025)
- [Towards Unification of Hallucination Detection and Fact Verification for Large Language Models](https://mlsanity.com/paper/2512.02772/) (2025)
- [TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG](https://mlsanity.com/paper/2512.07515/) (2025)
- [Who Checks the Citations? Benchmarking Legal Hallucination Detection](https://mlsanity.com/paper/2606.21155/) (2026)
- [HalluCounter: Reference-free LLM Hallucination Detection in the Wild!](https://mlsanity.com/paper/2503.04615/) (2025)
- [Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation](https://mlsanity.com/paper/2505.12265/) (2025)
- [Eidoku: A Neuro-Symbolic Verification Gate for LLM Reasoning via Structural Constraint Satisfaction](https://mlsanity.com/paper/2512.20664/) (2025)
- [Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection](https://mlsanity.com/paper/2601.19245/) (2026)
- [MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations](https://mlsanity.com/paper/2506.01367/) (2025)
- [Conservative Bias in Large Language Models: Measuring Relation Predictions](https://mlsanity.com/paper/2506.08120/) (2025)
- [Towards Building General Purpose Embedding Models for Industry 4.0 Agents](https://mlsanity.com/paper/2506.12607/) (2025)
- [Abstract Meaning Representation for Hospital Discharge Summarization](https://mlsanity.com/paper/2506.14101/) (2025)
- [Fake or Real: The Impostor Hunt in Texts for Space Operations](https://mlsanity.com/paper/2507.13508/) (2025)
- [Enhancing Hallucination Detection via Future Context](https://mlsanity.com/paper/2507.20546/) (2025)
- [A Concise Review of Hallucinations in LLMs and their Mitigation](https://mlsanity.com/paper/2512.02527/) (2025)
- [Disentangling Deception and Hallucination Failures in LLMs](https://mlsanity.com/paper/2602.14529/) (2026)
- [HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models](https://mlsanity.com/paper/2603.05828/) (2026)
- [AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2606.29545/) (2026)
- [CodeTD: Topology of Attention Detects Hallucinations in Code LLMs](https://mlsanity.com/paper/2609.07779/) (2026)
- [Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation](https://mlsanity.com/paper/2609.13581/) (2026)
- [GraphRAG-LTM: A Layered Threat Modeling Framework for Attacks on Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2610.19549/) (2026)

## Abstract (arXiv)

Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons. One critical factor hindering their widespread adoption is the occurrence of hallucinations, where LLMs invent answers that sound realistic, yet drift away from factual truth. In this paper, we present a novel method for detecting hallucinations in large language models, which tackles a critical issue in the adoption of these models in various real-world scenarios. Through extensive evaluations across multiple datasets and LLMs, including Llama-2, we study the hallucination levels of various recent LLMs and demonstrate the effectiveness of our method to automatically detect them. Notably, we observe up to 87% hallucinations for Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy of 81%, all without relying on external knowledge.