Temporal Knowledge Graph Question Answering (TKGQA) aims to answer time-sensitive questions by leveraging factual information from Temporal Knowledge Graphs (TKGs). While previous studies have employed pre-trained TKG embeddings or graph neural networks to inject temporal knowledge, they fail to fully understand the complex semantic information of time constraints. Recently, Large Language Models (LLMs) have shown remarkable progress, benefiting from their strong semantic understanding and reasoning generalization capabilities. However, their temporal reasoning ability remains limited. LLMs frequently suffer from hallucination and a lack of knowledge. To address these limitations, we propose the Plan of Knowledge framework with a contrastive temporal retriever, which is named PoK. Specifically, the proposed Plan of Knowledge module decomposes a complex temporal question into a sequence of sub-objectives from the pre-defined tools, serving as intermediate guidance for reasoning exploration. In parallel, we construct a Temporal Knowledge Store (TKS) with a contrastive retrieval framework, enabling the model to selectively retrieve semantically and temporally aligned facts from TKGs. By combining structured planning with temporal knowledge retrieval, PoK effectively enhances the interpretability and factual consistency of temporal reasoning. Extensive experiments on four benchmark TKGQA datasets demonstrate that PoK significantly improves the retrieval precision and reasoning accuracy of LLMs, surpassing the performance of the state-of-the-art TKGQA methods by 56.0% at most.
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Institute for Intelligent Computing, Alibaba Group（阿里巴巴集团智能计算研究院）
Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems
University of Pittsburgh（匹兹堡大学）；School of Computing and Information, University of Pittsburgh（匹兹堡大学计算与信息学院）；Clinical and Translational Science Institute, University of Pittsburgh（匹兹堡大学临床与转化科学研究所）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for 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）；Unisound（云知声）
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
State Key Laboratory for Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学，中国）；University of California, Merced, USA（加州大学默塞德分校，美国）；National Institute of Healthcare Data Science, Nanjing University, China（健康数据科学国家研究院，南京大学，中国）
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering
National University of Science and Technology POLITEHNICA Bucharest, Faculty of Automatic Control and Computers（波兰技术大学布加勒斯特分校国家科学与技术大学，自动控制与计算机学院）；Technical University of Munich（慕尼黑技术大学）；Paris 1 Panthéon-Sorbonne University（巴黎第一大学）；University of Bucharest（布加勒斯特大学）
LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context 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）；Zhipu AI（智谱AI）；Tsinghua University（清华大学）
A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China（大数据与决策实验室，国防科技大学，长沙，中国）；Baidu Inc., Beijing, China（百度公司，北京，中国）；Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China（计算机科学与技术系，哈尔滨工业大学（深圳），深圳，中国）
KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）科学与工程学院）；School of Microelectronics, Xi'an Jiaotong University, China（西安交通大学微电子学院）
Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
School of Engineering Stanford University Stanford, CA, USA；Simon Business School University of Rochester Rochester, NY, USA；Accounting \& Information Systems Rutgers University Newark, NJ, USA；Institute for Social；Economic Research；Policy Columbia University New York, NY, USA；Department of Economics Northeastern University Boston, MA, USA
