Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification approaches. As zero-shot topic extractors, LLMs are expected to understand human instructions to generate relevant and non-hallucinated topics based on the given documents. However, LLM-based topic modelling approaches often face difficulties in generating topics with adherence to granularity as specified in human instructions, often resulting in many near-duplicate topics. Furthermore, methods for addressing hallucinated topics generated by LLMs have not yet been investigated. In this paper, we focus on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling. To this end, we introduce a novel approach that leverages Direct Preference Optimisation (DPO) to fine-tune open-source LLMs, such as Mistral-7B. Our approach does not rely on traditional human annotation to rank preferred answers but employs a reconstruction pipeline to modify raw topics generated by LLMs, thus enabling a fast and efficient training and inference framework. Comparative experiments show that our fine-tuning approach not only significantly improves the LLM's capability to produce more coherent, relevant, and precise topics, but also reduces the number of hallucinated topics.
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
Institute for Clarity in Documentation（文献清晰研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康校区）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
Unified Hallucination Detection for Multimodal Large Language Models
Ant Group（蚂蚁集团）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
Hong Kong Baptist University（香港浸会大学）；UC Santa Barbara（加州大学圣巴巴拉分校）；UC Berkeley（加州大学伯克利分校）；Alibaba Group（阿里巴巴集团）；The University of Tokyo（东京大学）；University of Alberta（阿尔伯塔大学）
"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak
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）；UCLA（加州大学洛杉矶分校）；University of California, Merced（加州大学默塞德分校）；Tsinghua University（清华大学）
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
Huazhong University of Science and Technology（华中科技大学）；National University of Singapore（新加坡国立大学）；University of New South Wales（新南威尔士大学）；Nanyang Technological University（南洋理工大学）
Mitigating Object Hallucination via Robust Local Perception Search
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China（未来媒体中心及电子科技大学计算机科学与工程学院）
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Technical University of Munich（慕尼黑工业大学）；CISPA Helmholtz Center for Information Security（亥姆霍兹信息安全研究中心CISPA）；University of Stavanger（斯塔万格大学）；Fraunhofer FOKUS（弗劳恩霍夫FOKUS研究所）；The University of Tokyo（东京大学）；University of Alberta（阿尔伯塔大学）
A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination
Biomedical Informatics and Medical Education（生物医学信息学与医学教育）；Health AI（健康人工智能）；Department of Information Sciences and Technology（信息科学与技术系）；Department of Linguistics（语言学系）
Investigating Hallucination in Conversations for Low Resource Languages
University of North Alabama（北阿拉巴马大学）；Wichita State University（威奇托州立大学）；Murray State University（默里州立大学）；California State Polytechnic University Pomona（加州州立理工大学波莫纳分校）；Auburn University at Montgomery（奥本大学蒙哥马利分校）；Auburn University（奥本大学）；Gustavus Adolphus College（古斯塔夫阿道夫学院）；Stanford University（斯坦福大学）；Meta；Amazon GenAI（亚马逊生成式人工智能）
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；University of Oxford（牛津大学）；Council on Economic Policies（经济政策委员会）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Eawag: Swiss Federal Institute of Aquatic Science（瑞士联邦水科学与技术研究所）；Swiss Finance Institute (SFI)（瑞士金融研究所）
Fact-Level Confidence Calibration and Self-Correction
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）；Penn State University（宾夕法尼亚州立大学）
