Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aware Direct Preference Optimization), a simple extension of DPO that uses only binary factuality labels. F-DPO (i) applies a label-flipping transformation that corrects misordered preference pairs so the chosen response is never less factual than the rejected one, and (ii) adds a factuality-aware margin that emphasizes pairs with clear correctness differences, while reducing to standard DPO when both responses share the same factuality. We construct factuality-aware preference data by augmenting DPO pairs with binary factuality indicators and synthetic hallucinated variants. Across seven open-weight LLMs (1B-14B), F-DPO consistently improves factuality and reduces hallucination rates relative to both base models and standard DPO. On Qwen3-8B, F-DPO reduces hallucination rates by 5x(from 0.424 to 0.084) while improving factuality scores by 50% (from 5.26 to 7.90). F-DPO also generalizes to out-of-distribution benchmarks: on TruthfulQA, Qwen2.5-14B achieves +17% MC1 accuracy (0.500 to 0.585) and +49% MC2 accuracy (0.357 to 0.531). F-DPO requires no auxiliary reward model, token-level annotations, or multi-stage training.
DHI: Leveraging Diverse Hallucination Induction for Enhanced Contrastive Factuality Control in Large Language Models
School of Computer Science, Wuhan University（武汉大学计算机学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models
AGI Institute, School of Computer Science, Shanghai Jiao Tong University（AGI研究院，计算机科学学院，上海交通大学）；Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3（上海可信数据流通与治理Web3重点实验室）；School of Media & Communication, Shanghai Jiao Tong University（媒体与传播学院，上海交通大学）
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；DIRO, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；School of Information, Renmin University of China（中国人民大学信息资源管理学院）
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation
Sun Yat-Sen University（中山大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；The Hong Kong Polytechnic University（香港理工大学）；UNSW, Sydney, NSW 2052, Australia（新南威尔士大学）；Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳高性能数据挖掘重点实验室，深圳先进技术研究院，中国科学院）
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh（乌姆特国际大学计算机科学与工程系）；Department of Computer Science and Engineering, Daffodil International University, Dhaka-1341, Bangladesh（达芙尼国际大学计算机科学与工程系）；Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia（查尔斯·达尔文大学科学与技术学院）
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）；Hugging Face；Department of Computer Science, University College London（伦敦大学学院计算机系）；Together AI
School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；MILA（蒙特利尔学习算法研究所）
State-of-the-art abstractive summarization systems often generate \emph{hallucinations}; i.e., content that is not directly inferable from the source text. Despite being assumed incorrect, we find that much hallucinated content is factual, namely consistent with world knowledge. These factual hallucinations can be beneficial in a summary by providing useful background information. In this work, we propose a novel detection approach that separates factual from non-factual hallucinations of entities. Our method utilizes an entity's prior and posterior probabilities according to pre-trained and f…
Exploring and Mitigating Fawning Hallucinations in Large Language Models
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；Tianjin University（天津大学）；The Hong Kong University of Science（香港科学大学）
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Merced（加州大学默塞德分校）；University of Science and Technology of China（中国科学技术大学）
