Saved in:
| Main Authors: | Tan, Likun, Huang, Kuan-Wei, Shi, Joy, Wu, Kevin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.21538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
by: Tan, Likun, et al.
Published: (2025)
by: Tan, Likun, et al.
Published: (2025)
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
by: Sun, Zhongxiang, et al.
Published: (2024)
by: Sun, Zhongxiang, et al.
Published: (2024)
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
by: Chang, Tyler A., et al.
Published: (2024)
by: Chang, Tyler A., et al.
Published: (2024)
Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation
by: Jiang, Zhouyu, et al.
Published: (2025)
by: Jiang, Zhouyu, et al.
Published: (2025)
Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph
by: Hu, Jianpeng, et al.
Published: (2026)
by: Hu, Jianpeng, et al.
Published: (2026)
Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
by: Ding, Hanxing, et al.
Published: (2024)
by: Ding, Hanxing, et al.
Published: (2024)
Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
by: Lee, Zhan Peng, et al.
Published: (2025)
by: Lee, Zhan Peng, et al.
Published: (2025)
Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment
by: Li, Shanghao, et al.
Published: (2025)
by: Li, Shanghao, et al.
Published: (2025)
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation
by: Hu, Haichuan, et al.
Published: (2024)
by: Hu, Haichuan, et al.
Published: (2024)
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation
by: Guo, Hanghui, et al.
Published: (2025)
by: Guo, Hanghui, et al.
Published: (2025)
Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation
by: Zhang, Qianchi, et al.
Published: (2026)
by: Zhang, Qianchi, et al.
Published: (2026)
Retrieval-Augmented Multimodal Depression Detection
by: Hou, Ruibo, et al.
Published: (2025)
by: Hou, Ruibo, et al.
Published: (2025)
MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
by: Wu, Wenlong, et al.
Published: (2025)
by: Wu, Wenlong, et al.
Published: (2025)
SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusion
by: Wang, Lei
Published: (2025)
by: Wang, Lei
Published: (2025)
LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals
by: Yeh, Samuel, et al.
Published: (2025)
by: Yeh, Samuel, et al.
Published: (2025)
Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
by: Chu, Yun-Wei, et al.
Published: (2025)
by: Chu, Yun-Wei, et al.
Published: (2025)
Hallucination Detection and Hallucination Mitigation: An Investigation
by: Luo, Junliang, et al.
Published: (2024)
by: Luo, Junliang, et al.
Published: (2024)
Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation
by: Belikova, Julia, et al.
Published: (2026)
by: Belikova, Julia, et al.
Published: (2026)
Enhancing Frame Detection with Retrieval Augmented Generation
by: Diallo, Papa Abdou Karim Karou, et al.
Published: (2025)
by: Diallo, Papa Abdou Karim Karou, et al.
Published: (2025)
Disabling Self-Correction in Retrieval-Augmented Generation via Stealthy Retriever Poisoning
by: Dai, Yanbo, et al.
Published: (2025)
by: Dai, Yanbo, et al.
Published: (2025)
Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
by: Qu, Xiaoye, et al.
Published: (2024)
by: Qu, Xiaoye, et al.
Published: (2024)
REFIND at SemEval-2025 Task 3: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models
by: Lee, DongGeon, et al.
Published: (2025)
by: Lee, DongGeon, et al.
Published: (2025)
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation
by: Zhang, Linhai, et al.
Published: (2025)
by: Zhang, Linhai, et al.
Published: (2025)
Chain-of-Retrieval Augmented Generation
by: Wang, Liang, et al.
Published: (2025)
by: Wang, Liang, et al.
Published: (2025)
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
by: Niu, Cheng, et al.
Published: (2023)
by: Niu, Cheng, et al.
Published: (2023)
Corrective Retrieval Augmented Generation
by: Yan, Shi-Qi, et al.
Published: (2024)
by: Yan, Shi-Qi, et al.
Published: (2024)
Detecting Hallucinations in Large Language Models via Internal Attention Divergence Signals
by: van Dijk, Gijs
Published: (2026)
by: van Dijk, Gijs
Published: (2026)
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination
by: Rakin, Salman, et al.
Published: (2024)
by: Rakin, Salman, et al.
Published: (2024)
Time Course MechInterp: Analyzing the Evolution of Components and Knowledge in Large Language Models
by: Hakimi, Ahmad Dawar, et al.
Published: (2025)
by: Hakimi, Ahmad Dawar, et al.
Published: (2025)
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
by: Binkowski, Jakub, et al.
Published: (2026)
by: Binkowski, Jakub, et al.
Published: (2026)
Searching for Best Practices in Retrieval-Augmented Generation
by: Wang, Xiaohua, et al.
Published: (2024)
by: Wang, Xiaohua, et al.
Published: (2024)
BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection
by: You, Saukun Thika, et al.
Published: (2026)
by: You, Saukun Thika, et al.
Published: (2026)
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
by: Li, Xiang, et al.
Published: (2024)
by: Li, Xiang, et al.
Published: (2024)
HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generation
by: Bergeron, Loris, et al.
Published: (2025)
by: Bergeron, Loris, et al.
Published: (2025)
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation
by: Yan, Shi-Qi, et al.
Published: (2025)
by: Yan, Shi-Qi, et al.
Published: (2025)
Span-Level Hallucination Detection for LLM-Generated Answers
by: Elchafei, Passant, et al.
Published: (2025)
by: Elchafei, Passant, et al.
Published: (2025)
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
by: Wang, Chenyu, et al.
Published: (2026)
by: Wang, Chenyu, et al.
Published: (2026)
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation
by: Khan, Saadat Hasan, et al.
Published: (2026)
by: Khan, Saadat Hasan, et al.
Published: (2026)
Bolster Hallucination Detection via Prompt-Guided Data Augmentation
by: Li, Wenyun, et al.
Published: (2025)
by: Li, Wenyun, et al.
Published: (2025)
Optimization of Retrieval-Augmented Generation Context with Outlier Detection
by: Bulgakov, Vitaly
Published: (2024)
by: Bulgakov, Vitaly
Published: (2024)
Similar Items
-
FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
by: Tan, Likun, et al.
Published: (2025) -
ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
by: Sun, Zhongxiang, et al.
Published: (2024) -
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
by: Chang, Tyler A., et al.
Published: (2024) -
Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation
by: Jiang, Zhouyu, et al.
Published: (2025) -
Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph
by: Hu, Jianpeng, et al.
Published: (2026)