Cover Image

Neural Probe-Based Hallucination Detection for Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liang, Shize, Wang, Hongzhi
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models
by: Liang, Shize, et al.
Published: (2026)

Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models
by: Feng, Yijun
Published: (2025)

Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models
by: Liu, Qiang, et al.
Published: (2025)

Probing Neural Topology of Large Language Models
by: Zheng, Yu, et al.
Published: (2025)

Reference-free Hallucination Detection for Large Vision-Language Models
by: Li, Qing, et al.
Published: (2024)

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
by: Chen, Yuyan, et al.
Published: (2024)

Alleviating Hallucinations of Large Language Models through Induced Hallucinations
by: Zhang, Yue, et al.
Published: (2023)

Hallucination Detection with Small Language Models
by: Cheung, Ming
Published: (2025)

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
by: Su, Weihang, et al.
Published: (2024)

HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
by: Chen, Boshui, et al.
Published: (2026)

An Evolutionary Large Language Model for Hallucination Mitigation
by: Boulesnane, Abdennour, et al.
Published: (2024)

Unified Hallucination Detection for Multimodal Large Language Models
by: Chen, Xiang, et al.
Published: (2024)

Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models
by: Zhang, Shuo, et al.
Published: (2025)

Cross-Layer Attention Probing for Fine-Grained Hallucination Detection
by: Suresh, Malavika, et al.
Published: (2025)

(Im)possibility of Automated Hallucination Detection in Large Language Models
by: Karbasi, Amin, et al.
Published: (2025)

Scalable Token-Level Hallucination Detection in Large Language Models
by: Min, Rui, et al.
Published: (2026)

DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
by: Chen, Kedi, et al.
Published: (2024)

NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models
by: Ho, Zheng Yi, et al.
Published: (2024)

Copy-Paste to Mitigate Large Language Model Hallucinations
by: Long, Yongchao, et al.
Published: (2025)

The Impact of Negated Text on Hallucination with Large Language Models
by: Seo, Jaehyung, et al.
Published: (2025)

Theoretical Foundations and Mitigation of Hallucination in Large Language Models
by: Gumaan, Esmail
Published: (2025)

ANAH: Analytical Annotation of Hallucinations in Large Language Models
by: Ji, Ziwei, et al.
Published: (2024)

Confabulation: The Surprising Value of Large Language Model Hallucinations
by: Sui, Peiqi, et al.
Published: (2024)

PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
by: Deng, Kunquan, et al.
Published: (2024)

Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
by: Nguyen, Vy, et al.
Published: (2025)

Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
by: Lv, Qitan, et al.
Published: (2024)

How Large Language Models are Designed to Hallucinate
by: Ackermann, Richard, et al.
Published: (2025)

Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective
by: Sun, Zhongxiang, et al.
Published: (2025)

Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
by: Sato, Makoto
Published: (2025)

Principled Detection of Hallucinations in Large Language Models via Multiple Testing
by: Li, Jiawei, et al.
Published: (2025)

Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
by: Mündler, Niels, et al.
Published: (2023)

Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

Beyond Facts: Evaluating Intent Hallucination in Large Language Models
by: Hao, Yijie, et al.
Published: (2025)

HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models
by: Nananukul, Navapat, et al.
Published: (2023)

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
by: Chrysostomou, George, et al.
Published: (2023)

HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models
by: Jiang, Xinyan, et al.
Published: (2025)

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models
by: Pandit, Shrey, et al.
Published: (2025)

ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs
by: Zhang, Zhenliang, et al.
Published: (2025)

Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
by: Wang, Xintong, et al.
Published: (2024)

Neural Diversity Regularizes Hallucinations in Language Models
by: Chakrabarti, Kushal, et al.
Published: (2025)