Despite advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, their effectiveness is often hindered by a lack of integration with entity relationships and community structures, limiting their ability to provide contextually rich and accurate information retrieval for fact-checking. We introduce CommunityKG-RAG (Community Knowledge Graph-Retrieval Augmented Generation), a novel zero-shot framework that integrates community structures within Knowledge Graphs (KGs) with RAG systems to enhance the fact-checking process. Capable of adapting to new domains and queries without additional training, CommunityKG-RAG utilizes the multi-hop nature of community structures within KGs to significantly improve the accuracy and relevance of information retrieval. Our experimental results demonstrate that CommunityKG-RAG outperforms traditional methods, representing a significant advancement in fact-checking by offering a robust, scalable, and efficient solution.
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS
Chongqing University（重庆大学）；Queen Mary University of London（伦敦玛丽女王大学）；Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing（重庆市大数据智能与隐私计算重点实验室）；Fangda Partners（方达律师事务所）
CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京人工智能教育重点实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University（清华大学人工智能研究院计算机科学与技术系）；Northeastern University（东北大学）
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
IIT Kharagpur（印度理工学院克勒格布尔分校）；Aditya English Medium School（阿迪蒂亚英语中学）；UCLA（加利福尼亚大学洛杉矶分校）；Stanford University（斯坦福大学）；University of South Carolina（南卡罗来纳大学）；Amazon GenAI（亚马逊生成式AI部门）
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）；appliedAI Institute for Europe gGmbH（欧洲appliedAI研究所（非营利有限责任公司））；appliedAI Initiative GmbH（appliedAI倡议有限责任公司）
CUB: Benchmarking Context Utilisation Techniques for Language Models
Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）；Seoul National University（首尔国立大学）；University of Copenhagen（哥本哈根大学）；Ewha Womans University（成均馆大学）
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）
IBM Research AI（IBM研究院人工智能部门）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-bas…
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation
University of Florida（佛罗里达大学）；National Institute of Technology Calicut（卡利卡特国家理工学院）；SRM, Chennai（SRM大学（金奈））；University of Cincinnati（辛辛那提大学）；Dartmouth College（达特茅斯学院）；Arizona State University（亚利桑那州立大学）
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Portland State University（波特兰州立大学）；Renmin University of China（中国人民大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Dalian Maritime University（大连海事大学）；University of Kansas（堪萨斯大学）
