Unsupervised Hallucination Detection by Inspecting Reasoning Processes (2025.emnlp-main)
Copied to clipboard
| Challenge: | Unsupervised hallucination detection aims to identify hallucines generated by large language models without relying on labeled data. |
| Approach: | They propose an unsupervised method to detect hallucinated content by large language models . they use internal representations intrinsic to factual correctness to prompt the model to verify the truthfulness of a given statement . |
| Outcome: | The proposed framework outperforms existing unsupervised methods and is fully unsupervised and low cost. |
Similar Papers
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on hallucination detection for LLMs focus on how to identify possible factrelated errors in outputs. |
| Approach: | They propose an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods in hallucination detection. |
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
| Approach: | They propose a hallucination detection benchmark and a method to detect hallucines in LLMs. |
| Outcome: | The proposed method detects hallucinations and mitigates them using different training stages. |
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)
Copied to clipboard
Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu
| Challenge: | Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification. |
| Approach: | They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency. |
| Outcome: | The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information. |
Towards Long Context Hallucination Detection (2025.findings-naacl)
Copied to clipboard
Siyi Liu, Kishaloy Halder, Zheng Qi, Wei Xiao, Nikolaos Pappas, Phu Mon Htut, Neha Anna John, Yassine Benajiba, Dan Roth
| Challenge: | Large language models are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. |
| Approach: | They propose a dataset specifically designed for long-context hallucination detection. |
| Outcome: | The proposed architecture outperforms existing models while providing faster inference. |
Hallucination Detection in Long-Form Text Generated by LLMs: A Benchmark and a Hyper-Relational Knowledge Graph Approach (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection are coarse-grained and lack long-range consistency checks. |
| Approach: | They propose a benchmark for long-form hallucination detection that incorporates diverse entity types and intricate factual dependencies spanning extended contexts. |
| Outcome: | The proposed framework outperforms baselines and robustly integrates fact-centric hyper-relational knowledge graphs. |
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs (2025.emnlp-main)
Copied to clipboard
Denis Janiak, Jakub Binkowski, Albert Sawczyn, Bogdan Gabrys, Ravid Shwartz-Ziv, Tomasz Jan Kajdanowicz
| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their tendency to hallucinate poses serious challenges for reliable deployment. |
| Approach: | They propose to use ROUGE to assess lexical overlap to determine accuracy of hallucination detection methods. |
| Outcome: | The proposed evaluation frameworks can rival complex methods, exposing a fundamental flaw in current evaluation practices. |
A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation (2025.naacl-long)
Copied to clipboard
| Challenge: | Current large language models (LLMs) produce factually incorrect statements . |
| Approach: | They propose a probabilistic framework for LLM hallucination detection that generates a belief tree by expanding a statement into logically related claims and reasoning globally about the relationships between these claims. |
| Outcome: | The proposed method improves on multiple hallucination detection benchmarks by 3%-9% over state-of-the-art models. |
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for detecting hallucinations in large language models are limited due to their high frequency and high accuracy. |
| Approach: | They propose a method to detect hallucinations in large language models by repeating model-generated responses from its generated answer. |
| Outcome: | The proposed method achieves 87% hallucinations in a specific experiment without external knowledge. |
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (2023.emnlp-main)
Copied to clipboard
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Recent advances in Large Language Models have generated widespread acclaim, but hallucination has also emerged as a by-product. |
| Approach: | They propose a fine-grained discourse on profiling hallucination based on its degree, orientation, and category . they categorize hallucines into six types: acronym ambiguity, generated golem, virtual voice, geographic erratum, time wrap . |
| Outcome: | The proposed method categorizes hallucination into six types based on their degree, orientation, and category . |
The Impact of Negated Text on Hallucination with Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. |
| Approach: | They propose to examine whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases. |
| Outcome: | The proposed model can detect hallucinations comparable to affirmative cases, but it is difficult to detect them in negated text, the authors show . |