Fetching the paper…
Reading the bibliography…
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts.
Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 1905
Earlier work this paper cites.
Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 1905
Earlier work this paper cites.
Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019b · 1906
Earlier work this paper cites.
Do multi-hop question answering systems know how to answer the single-hop sub-questions?
Yixuan Tang, Hwee Tou Ng, and Anthony KH Tung. 2020 · 2002
Earlier work this paper cites.
Is graph structure necessary for multi-hop reasoning?
Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, and Guoping Hu. 2020 · 2004
Earlier work this paper cites.
The web as a knowledge-base for answering complex questions
A. Talmor and J. Berant. 2018 · 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 Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018a · 2018
Earlier work this paper cites.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Earlier work this paper cites.
Hierarchical graph network for multi-hop question answering
Yuwei Fang, S. Sun, Zhe Gan, Rohit Radhakrishna Pillai, Shuohang Wang, and Jingjing Liu. 2019 · 2019
Earlier work this paper cites.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdel rahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Earlier work this paper cites.
Dynamically fused graph network for multi-hop reasoning
Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 2019
Earlier work this paper cites.
Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Earlier work this paper cites.
Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou. 2020 · 2020
Earlier work this paper cites.
Decomposing complex questions makes multi-hop QA easier and more interpretable
Ruiliu Fu, Han Wang, Xuejun Zhang, Jun Zhou, and Yonghong Yan. 2021 · 2021
Cited alongside, same era.
DeBERTa: Decoding-enhanced BERT with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Edg-based question decomposition for complex question answering over knowledge bases
Xixin Hu, Yiheng Shu, Xiang Huang, and Yuzhong Qu. 2021 · 2021
Cited alongside, same era.
Breadth first reasoning graph for multi-hop question answering
Yongjie Huang and Meng Yang. 2021 · 2021
Cited alongside, same era.
Text modular networks: Learning to decompose tasks in the language of existing models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2021 · 2021
Cited alongside, same era.
Asynchronous multi-grained graph network for interpretable multi-hop reading comprehension
MuSiQue: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
Later among the works it cites.
Locate then ask: Interpretable stepwise reasoning for multi-hop question answering
Siyuan Wang, Zhongyu Wei, Zhihao Fan, Qi Zhang, and Xuanjing Huang. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Learning to decompose: Hypothetical question decomposition based on comparable texts
Ben Zhou, Kyle Richardson, Xiaodong Yu, and Dan Roth. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ronghan Li, Lifang Wang, Shengli Wang, and Zejun Jiang. 2021 · 2021
Cited alongside, same era.
What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Graph-free multi-hop reading comprehension: A select-to-guide strategy
Bohong Wu, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021
Cited alongside, same era.
Large language models are few(1)-shot table reasoners
Wenhu Chen. 2022 · 2022
Cited alongside, same era.
Complex reading comprehension through question decomposition
Xiao-Yu Guo, Yuan-Fang Li, and Gholamreza Haffari. 2022 · 2022
Cited alongside, same era.
Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, H. Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2022 · 2022
Cited alongside, same era.
Rainier: Reinforced knowledge introspector for commonsense question answering
Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Later among the works it cites.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023 · 2023
Later among the works it cites.
Pokemqa: Programmable knowledge editing for multi-hop question answering
Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, and Xin Wang. 2023 · 2023
Later among the works it cites.
Analyzing the effectiveness of the underlying reasoning tasks in multi-hop question answering
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2023 · 2023
Later among the works it cites.
Tab-cot: Zero-shot tabular chain of thought
Ziqi Jin and Wei Lu. 2023 · 2023
Later among the works it cites.
In-context ability transfer for question decomposition in complex qa
V Venktesh, Sourangshu Bhattacharya, and Avishek Anand. 2023 · 2023
Later among the works it cites.
Rethinking label smoothing on multi-hop question answering
Zhangyue Yin, Yuxin Wang, Xiannian Hu, Yiguang Wu, Hang Yan, Xinyu Zhang, Zhao Cao, Xuanjing Huang, and Xipeng Qiu. 2023 · 2023
Later among the works it cites.
Beam retrieval: General end-to-end retrieval for multi-hop question answering
Jiahao Zhang, Haiyang Zhang, Dongmei Zhang, Yong Liu, and Shen Huang. 2023 · 2023
Later among the works it cites.
Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries
Yixuan Tang and Yi Yang. 2024 · 2024
Closest in time.