Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to relevant sources alongside generated content, enhancing the verifiability of generations. However, citing passages accurately in answers remains a substantial challenge. This paper proposes a weakly-supervised fine-tuning method leveraging factual consistency models (FCMs). Our approach alternates between generating texts with citations and supervised fine-tuning with FCM-filtered citation data. Focused learning is integrated into the objective, directing the fine-tuning process to emphasise the factual unit tokens, as measured by an FCM. Results on the ALCE few-shot citation benchmark with various instruction-tuned LLMs demonstrate superior performance compared to in-context learning, vanilla supervised fine-tuning, and state-of-the-art methods, with an average improvement of $34.1$, $15.5$, and $10.5$ citation F$_1$ points, respectively. Moreover, in a domain transfer setting we show that the obtained citation generation ability robustly transfers to unseen datasets. Notably, our citation improvements contribute to the lowest factual error rate across baselines.
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation
Smith School of Business, Queen’s University（女王大学史密斯商学院）；Ingenuity Labs Research Institute, Queen’s University（女王大学 Ingenuity 实验室研究所）；Department of Electrical and Computer Engineering, Queen’s University（女王大学电气与计算机工程系）
How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（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）；Beijing University of Post and Telecommunications（北京邮电大学）
Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，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）；University of Chinese Academy of Sciences（中国科学院大学）；Unisound Al Technology Co,Ltd（Unisound人工智能技术有限公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models
Zhipu AI（智谱AI）；Zhongguancun Laboratory（中关村实验室）；Renmin University of China（中国人民大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）
Citekit: A Modular Toolkit for Large Language Model Citation Generation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Co-occurrence is not Factual Association in Language Models
Department of Electronics Engineering（电子工程系）；Tsinghua University（清华大学）；Department of Electronics Engineering, College of AI（人工智能学院电子工程系）；Beijing National Research Center for Information Science and Technology（北京信息科学国家研究中心）；Center for Big Data and Clinical Research, Institute for Precision Medicine（精准医学研究院大数据与临床研究中心）
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
The Chinese University of Hong Kong（香港中文大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））；MoE Key Laboratory of High Confidence Software Technologies（教育部高可信软件技术重点实验室）
Evaluating Reliability Asymmetries in Chinese Factual Search and AI Answers
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工学院）；University of Science and Technology of China（中国科学技术大学）
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学首都人工智能学院）；AI Lab at Lenovo Research（联想研究院人工智能实验室）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际商务与经济大学人工智能与数据科学学院）
ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents
Multiverse Computing；Parque Cientifico y Tecnológico de Gipuzkoa（吉普斯夸科技园）；Centre for Social Innovation（社会创新中心）；Donostia International Physics Center（多诺斯蒂亚国际物理中心）；Ikerbasque Foundation for Science（伊克尔巴斯克科学基金会）
