Fetching the paper…
Reading the bibliography…
Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies.
SPARQA: skeleton-based semantic parsing for complex questions over knowledge bases
Yawei Sun, Lingling Zhang, Gong Cheng, and Yuzhong Qu · 2003
Earlier work this paper cites.
Kilt: a benchmark for knowledge intensive language tasks, 2021
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel · 2009
Earlier work this paper cites.
Thinking, fast and slow
Daniel Kahneman · 2011
Earlier work this paper cites.
The value of semantic parse labeling for knowledge base question answering
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh · 2016
Earlier work this paper cites.
Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Earlier work this paper cites.
Knowledge enhanced contextual word representations
Matthew E Peters, Mark Neumann, Robert L Logan IV, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A Smith · 2019
Earlier work this paper cites.
Reasoning step-by-step: Temporal sentence localization in videos via deep rectification-modulation network
Daizong Liu, Xiaoye Qu, Jianfeng Dong, and Pan Zhou · 2020
Earlier work this paper cites.
Creak: A dataset for commonsense reasoning over entity knowledge, 2021
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, and Greg Durrett · 2021
Earlier work this paper cites.
The unreliability of explanations in few-shot prompting for textual reasoning
Xi Ye and Greg Durrett · 2022
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection, 2023
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
Cited alongside, same era.
Benchmarking large language models in retrieval-augmented generation, 2023
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun · 2023
Cited alongside, same era.
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey, 2024
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang · 2024
Closest in time.
Joint multi-facts reasoning network for complex temporal question answering over knowledge graph
Rikui Huang, Wei Wei, Xiaoye Qu, Wenfeng Xie, Xianling Mao, and Dangyang Chen · 2024
Closest in time.
Efficient knowledge infusion via kg-llm alignment, 2024
Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai, Shuhan Luo, and Zhiqiang Zhang · 2024
Closest in time.
The integration of semantic and structural knowledge in knowledge graph entity typing
Muzhi Li, Minda Hu, Irwin King, and Ho-fung Leung · 2024
Closest in time.
Entropy-based decoding for retrieval-augmented large language models, 2024
Zexuan Qiu, Zijing Ou, Bin Wu, Jingjing Li, Aiwei Liu, and Irwin King · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal · 2023
Cited alongside, same era.
Qald-10–the 10th challenge on question answering over linked data
Ricardo Usbeck, Xi Yan, Aleksandr Perevalov, Longquan Jiang, Julius Schulz, Angelie Kraft, Cedric Möller, Junbo Huang, Jan Reineke, Axel-Cyrille Ngonga Ngomo, et al · 2023
Cited alongside, same era.
Chain-of-note: Enhancing robustness in retrieval-augmented language models, 2023
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu · 2023
Cited alongside, same era.
A survey on rag meets llms: Towards retrieval-augmented large language models
Yujuan Ding, Wenqi Fan, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
Cited alongside, same era.
From local to global: A graph rag approach to query-focused summarization, 2024
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson · 2024
Cited alongside, same era.
Retrieval, reasoning, re-ranking: A context-enriched framework for knowledge graph completion, 2024b
Muzhi Li, Cehao Yang, Chengjin Xu, Xuhui Jiang, Yiyan Qi, Jian Guo, Ho fung Leung, and Irwin King
Cited in the paper.
Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources
Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, and Lidong Bing
Cited in the paper.
Self-consistency improves chain of thought reasoning in language models, 2023a
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou
Cited in the paper.
Closest in time.
Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, and Dhagash Mehta · 2024
Closest in time.
Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph, 2024
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, and Jian Guo · 2024
Closest in time.
Retrieval-augmented generation for ai-generated content: A survey
Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui · 2024
Closest in time.