:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Song, Wang, Xun, Mei, Jie, Xie, Yujia, Muarray, Sean, Li, Zhang, Wu, Lingfeng, Chen, Si-Qing, Xiong, Wayne
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	https://arxiv.org/abs/2407.15441
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
por: Cheng, Xiaoxue, et al.
Publicado: (2025)

Hallucination Detection and Hallucination Mitigation: An Investigation
por: Luo, Junliang, et al.
Publicado: (2024)

Genetic Approach to Mitigate Hallucination in Generative IR
por: Kulkarni, Hrishikesh, et al.
Publicado: (2024)

NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors
por: Ren, Lingfeng, et al.
Publicado: (2026)

Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
por: Xie, Zequn, et al.
Publicado: (2026)

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
por: Liang, Xun, et al.
Publicado: (2023)

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
por: Xiong, Guangzhi, et al.
Publicado: (2025)

KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models
por: Liu, Zhongxin, et al.
Publicado: (2025)

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?
por: Yin, Hao, et al.
Publicado: (2025)

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

Hallucination Mitigating for Medical Report Generation
por: Zhao, Ruoqing, et al.
Publicado: (2026)

Analyzing and Mitigating Object Hallucination: A Training Bias Perspective
por: Li, Yifan, et al.
Publicado: (2025)

Rethinking Visual Neglect: Steering via Context-Preference for MLLM Hallucination Mitigation
por: Wu, Jingwen, et al.
Publicado: (2026)

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
por: Phillips, Edward, et al.
Publicado: (2025)

LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
por: Zhang, Ziyao, et al.
Publicado: (2024)

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

Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation
por: Liang, Yuxin, et al.
Publicado: (2024)

Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
por: Pham, Duy Khoa, et al.
Publicado: (2024)

FactCheckmate: Preemptively Detecting and Mitigating Hallucinations in LMs
por: Alnuhait, Deema, et al.
Publicado: (2024)

Mitigating Hallucination in Fictional Character Role-Play
por: Sadeq, Nafis, et al.
Publicado: (2024)

Mitigating Entity-Level Hallucination in Large Language Models
por: Su, Weihang, et al.
Publicado: (2024)

Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs
por: Wu, Wei, et al.
Publicado: (2024)

In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
por: Chen, Shiqi, et al.
Publicado: (2024)

Reducing Tool Hallucination via Reliability Alignment
por: Xu, Hongshen, et al.
Publicado: (2024)

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
por: Siddiqui, S M Tahmid, et al.
Publicado: (2026)

Mitigating Hallucinations in Large Language Models via Causal Reasoning
por: Li, Yuangang, et al.
Publicado: (2025)

Generating Effective CoT Traces for Mitigating Causal Hallucination
por: Zhao, Yiheng, et al.
Publicado: (2026)

GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation
por: Zhu, Runchuan, et al.
Publicado: (2025)

LLM Hallucination Detection: HSAD
por: Li, JinXin, et al.
Publicado: (2025)

MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
por: Wu, Wenlong, et al.
Publicado: (2025)

HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain
por: Anaokar, Spandan, et al.
Publicado: (2025)

ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
por: Sun, Siqi, et al.
Publicado: (2026)

Generation Constraint Scaling Can Mitigate Hallucination
por: Kollias, Georgios, et al.
Publicado: (2024)

Mitigating Large Language Model Hallucination with Faithful Finetuning
por: Hu, Minda, et al.
Publicado: (2024)

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
por: Wang, Shengyuan, et al.
Publicado: (2025)

Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection
por: Chen, Kedi, et al.
Publicado: (2025)

Confidence-Aware Routing for Large Language Model Reliability Enhancement: A Multi-Signal Approach to Pre-Generation Hallucination Mitigation
por: M, Nandakishor
Publicado: (2025)

Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
por: Ding, Hanxing, et al.
Publicado: (2024)

MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
por: Jia, Ao, et al.
Publicado: (2025)

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