Advancements in natural language processing have revolutionized the way we can interact with digital information systems, such as databases, making them more accessible. However, challenges persist, especially when accuracy is critical, as in the biomedical domain. A key issue is the hallucination problem, where models generate information unsupported by the underlying data, potentially leading to dangerous misinformation. This paper presents a novel approach designed to bridge this gap by combining Large Language Models (LLM) and Knowledge Graphs (KG) to improve the accuracy and reliability of question-answering systems, on the example of a biomedical KG. Built on the LangChain framework, our method incorporates a query checker that ensures the syntactical and semantic validity of LLM-generated queries, which are then used to extract information from a Knowledge Graph, substantially reducing errors like hallucinations. We evaluated the overall performance using a new benchmark dataset of 50 biomedical questions, testing several LLMs, including GPT-4 Turbo and llama3:70b. Our results indicate that while GPT-4 Turbo outperforms other models in generating accurate queries, open-source models like llama3:70b show promise with appropriate prompt engineering. To make this approach accessible, a user-friendly web-based interface has been developed, allowing users to input natural language queries, view generated and corrected Cypher queries, and verify the resulting paths for accuracy. Overall, this hybrid approach effectively addresses common issues such as data gaps and hallucinations, offering a reliable and intuitive solution for question answering systems. The source code for generating the results of this paper and for the user-interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui
HalluScore: Large Language Model Hallucination Question Answering Benchmark
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学信息与计算机科学系）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
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
AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油和矿物大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
International Digital Economy Academy（国际数字经济学院）；School of Software & Microelectronics, Peking University（北京大学软件与微电子学院）；National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；School of Computer Science, Peking University（北京大学计算机学院）；Data Science and Artificial Intelligence, Monash University（莫纳什大学数据科学与人工智能学院）；ModelTC
CCNU at SemEval-2025 Task 3: Leveraging Internal and External Knowledge of Large Language Models for Multilingual Hallucination Annotation
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning（湖北省人工智能与智能学习重点实验室）；National Language Resources Monitoring and Research Center for Network Media（网络媒体语言资源监测与研究中心）；School of Computer Science, Central China Normal University（中央财经大学计算机学院）
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；HKGAI under InnoHK（香港人工智能研究院（InnoHK框架下））；Hong Kong University of Science and Technology（香港科技大学）
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心（BCAI））；UNC Chapel-Hill（北卡罗来纳大学教堂山分校）
An Evolutionary Large Language Model for Hallucination Mitigation
BIOSTIM Laboratory Faculty of Medicine Salah Boubnider University Constantine, Algeria（BIOSTIM实验室医学院萨拉赫·布宾德大学阿尔及利亚科纳克特）；Department of IFA Faculty of NTIC Abdelhamid Mehri University Constantine, Algeria（IFA部门NTIC学院阿卜杜勒哈米德·梅赫里大学阿尔及利亚科纳克特）
Investigating Symbolic Triggers of Hallucination in Gemma Models Across HaluEval and TruthfulQA
Center for Artificial Intelligence in Medicine, Imaging and Forensics, Sharda University, Greater Noida, India（人工智能医学与影像及法医学中心，沙达大学，印度Greater Noida）；University of Maryland, Baltimore County, Baltimore, MD, USA（马里兰大学巴尔的摩县）
