Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering
King’s College London（伦敦国王学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程学院）
ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering
Indian Institute of Technology Patna（印度帕纳布理工大学）；Manipal University Jaipur（贾伊普尔曼尼帕尔大学）；Indian Institute of Science Education and Research Kolkata（印度科学教育与研究学院科利卡塔）
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
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空间安全学院）；Microsoft Research Asia（微软亚洲研究院）；College of Mathematics, Physics and Information Engineering, Jiaxing University（嘉兴大学数理与信息工程学院）
MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation
Data Clinic, University Hospital of Nantes, France（南特大学医院数据诊所，法国）；Nantes Université, École Centrale Nantes, CNRS, LS2N, France（南特大学，中央理工学院南特分校，国家科学研究中心，LS2N，法国）
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Alibaba Group（阿里巴巴集团）；School of Statistics, East China Normal University（华东师范大学统计学院）
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
Institute of Automation, Chinese Academy of Sciences（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）；The Hong Kong University of Science and Technology（香港科技大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
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）
Temporal Knowledge Question Answering via Abstract Reasoning Induction
Laboratory for Big Data and Decision, National University of Defense Technology（国防科技大学大数据与决策实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
National University of Singapore（新加坡国立大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Singapore General Hospital（新加坡中央医院）；Zhejiang Lab（之江实验室）；National Institutes of Health（美国国立卫生研究院）；Yale University（耶鲁大学）
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；SynthLabs；Carnegie Mellon University（卡内基梅隆大学）
Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
Department of Electrical Engineering；Computer Science University of Toledo Toledo, USA；Institute of Mathematical Sciences Claremont Graduate University Claremont, USA；Department of Bioengineering University of Toledo Toledo, USA；Department of Computer Science Bowling Green State University Bowling Green, USA
