Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP: retrieval-augmented generation, summarization, document-grounded dialogue, and more. Current approaches to this kind of fact-checking are based on verifying each piece of a model generation against potential evidence using an LLM. However, this process can be very computationally expensive, requiring many calls to a model to check a single response. In this work, we show how to build small fact-checking models that have GPT-4-level performance but for 400x lower cost. We do this by constructing synthetic training data with GPT-4, which involves creating realistic yet challenging instances of factual errors via a structured generation procedure. Training on this data teaches models to check each fact in the claim and recognize synthesis of information across sentences. For evaluation, we unify datasets from recent work on fact-checking and grounding LLM generations into a new benchmark, LLM-AggreFact. Our best system MiniCheck-FT5 (770M parameters) outperforms all systems of comparable size and reaches GPT-4 accuracy. We release LLM-AggreFact, code for data synthesis, and models.
RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild
School of Computing (SoC), National University of Singapore (NUS)（新加坡国立大学计算机学院（SoC））；National University of Singapore (NUS)（新加坡国立大学）；Department of Electrical and Computer Engineering (ECE), National University of Singapore (NUS)（新加坡国立大学电子与计算机工程系（ECE））
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital Rechts der Isar（慕尼黑工业大学 Rechts der Isar 大学医院）；German Heart Center Munich（慕尼黑德国心脏中心）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；German Consortium for Translational Cancer Research（德国转化癌症研究联盟）
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
University of Tokyo（东京大学）；Texas A&M University（德克萨斯A&M大学）；University of Cambridge（剑桥大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Aarhus University（阿贾克斯大学）；Yale University（耶鲁大学）
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time
University of Maryland, College Park（马里兰大学帕克分校）；University of Toronto（多伦多大学）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research, Singapore（新加坡科技研究局）；Indian Institute of Technology Delhi（印度理工学院德里分校）；Alibaba DAMO Academy（阿里巴巴达摩院）；Microsoft Research Asia（微软亚洲研究院）；Shanghai University of Finance and Economics（上海财经大学）；Inner Mongolia University（内蒙古大学）；Kyoto University（京都大学）；Jiangxi Normal University（江西师范大学）；Korea University（韩国大学）；Nanyang Technological University（南洋理工大学）
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
Golden Gate University, San Francisco Foundation（金门大学，旧金山基金会）；AgenticOS (FAOS)（AgenticOS（FAOS））；Associate Director, Data, Digital & IT Novartis Healthcare Pvt. Ltd.（数据、数字与IT部门，诺华健康有限公司）；Novartis Healthcare Pvt. Ltd., Hyderabad, India（诺华健康有限公司，海得拉巴，印度）
Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals
Tongji University（同济大学）；Shanghai AI Laboratory（上海人工智能实验室）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）；EPFL（苏黎世联邦理工学院）；Wuhan University（武汉大学）
CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Electrical and Electronic Engineering（电气与电子工程系）；Islamic University of Technology（伊斯兰技术大学）
Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes
School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University（信息与电子工程学院（Sussex人工智能研究院），浙江工商大学）；Sussex Artificial Intelligent Institute, Zhejiang Gongshang University（Sussex人工智能研究院，浙江工商大学）；Department of Engineering and Design, University of Sussex（工程与设计系， Sussex大学）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（人工智能研究院，深圳MSU-BIT大学）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
