Cover Image

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Junyi, Chen, Jie, Ren, Ruiyang, Cheng, Xiaoxue, Zhao, Wayne Xin, Nie, Jian-Yun, Wen, Ji-Rong
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
by: Cheng, Xiaoxue, et al.
Published: (2024)

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
by: Ren, Ruiyang, et al.
Published: (2023)

Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector
by: Cheng, Xiaoxue, et al.
Published: (2024)

REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
by: Wang, Yuhao, et al.
Published: (2024)

BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
by: Dong, Zican, et al.
Published: (2023)

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
by: Ren, Ruiyang, et al.
Published: (2024)

Self-Calibrated Listwise Reranking with Large Language Models
by: Ren, Ruiyang, et al.
Published: (2024)

Enhancing Cross-task Transfer of Large Language Models via Activation Steering
by: Tang, Xinyu, et al.
Published: (2025)

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
by: Cheng, Xiaoxue, et al.
Published: (2025)

Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models
by: Li, Junyi, et al.
Published: (2025)

Exploring Context Window of Large Language Models via Decomposed Positional Vectors
by: Dong, Zican, et al.
Published: (2024)

Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
by: Chen, Jie, et al.
Published: (2024)

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

PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning
by: Liu, Langming, et al.
Published: (2026)

Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
by: Sun, Haoxiang, et al.
Published: (2025)

A Survey of Large Language Models
by: Zhao, Wayne Xin, et al.
Published: (2023)

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
by: Wang, Xiaolei, et al.
Published: (2023)

Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment
by: Chen, Zhipeng, et al.
Published: (2024)

AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models
by: Cao, Zouying, et al.
Published: (2023)

Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design
by: Liu, Yihong, et al.
Published: (2025)

DHI: Leveraging Diverse Hallucination Induction for Enhanced Contrastive Factuality Control in Large Language Models
by: Guo, Jiani, et al.
Published: (2026)

Neuron-based Personality Trait Induction in Large Language Models
by: Deng, Jia, 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)

Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
by: Jiang, Jinhao, et al.
Published: (2024)

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

Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models
by: Li, Yifan, et al.
Published: (2024)

An Empirical Study on Eliciting and Improving R1-like Reasoning Models
by: Chen, Zhipeng, et al.
Published: (2025)

Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
by: Tang, Xinyu, et al.
Published: (2024)

Not All That Is Fluent Is Factual: Investigating Hallucinations of Large Language Models in Academic Writing
by: Khan, Humam, et al.
Published: (2026)

Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
by: Sakib, Shahnewaz Karim, et al.
Published: (2025)

Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
by: Ren, Ruiyang, et al.
Published: (2025)

Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models
by: Chen, Zhipeng, et al.
Published: (2024)

Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
by: Chen, Zhipeng, et al.
Published: (2024)

UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
by: Huang, Zhaoheng, et al.
Published: (2024)

Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning
by: Chen, Zhipeng, et al.
Published: (2026)

Decomposing the Entropy-Performance Exchange: The Missing Keys to Unlocking Effective Reinforcement Learning
by: Deng, Jia, et al.
Published: (2025)

DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning
by: Tang, Xinyu, et al.
Published: (2024)

Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
by: Lv, Ang, et al.
Published: (2024)

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
by: Jiang, Jinhao, et al.
Published: (2024)