# Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Facts

arXiv: https://arxiv.org/abs/2511.12236 · HTML: https://mlsanity.com/paper/2511.12236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://mlsanity.com/paper/2305.14739/) (2023)
- [Fine-tuning Language Models for Factuality](https://mlsanity.com/paper/2311.08401/) (2023)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency](https://mlsanity.com/paper/2311.01740/) (2023)
- [A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection](https://mlsanity.com/paper/2310.06498/) (2023)
- [InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers](https://mlsanity.com/paper/2403.02889/) (2024)
- [Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector](https://mlsanity.com/paper/2406.11277/) (2024)
- [SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully](https://mlsanity.com/paper/2401.05930/) (2024)
- [A Stable and Effective Learning Strategy for Trainable Greedy Decoding](https://mlsanity.com/paper/1804.07915/) (2018)

## Built on by (most cited first, 4 of 4)

- [All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection](https://mlsanity.com/paper/2601.04160/) (2026)
- [Hallucinations Live in Variance](https://mlsanity.com/paper/2601.07058/) (2026)
- [HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models](https://mlsanity.com/paper/2604.26139/) (2026)
- [R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration](https://mlsanity.com/paper/2609.11955/) (2026)

## Abstract (arXiv)

Large language models (LLMs), despite their remarkable text generation capabilities, often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge. This poses serious risks in domains like healthcare, finance, and customer support. A typical way to use LLMs is via the APIs provided by LLM vendors where there is no access to model weights or options to fine-tune the model. Existing methods to detect hallucinations in such settings where the model access is restricted or constrained by resources typically require making multiple LLM API calls, increasing latency and API cost. We introduce CONFACTCHECK, an efficient hallucination detection approach that does not leverage any external knowledge base and works on the simple intuition that responses to factual probes within the generated text should be consistent within a single LLM and across different LLMs. Rigorous empirical evaluation on multiple datasets that cover both the generation of factual texts and the open generation shows that CONFACTCHECK can detect hallucinated facts efficiently using fewer resources and achieves higher accuracy scores compared to existing baselines that operate under similar conditions. Our code is available here.