MoreHopQA: More Than Multi-hop Reasoning

The value of semantic parse labeling for knowledge base question answering

Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, and Jina Suh. 2016 · 2016

Earlier work this paper cites.

What makes reading comprehension questions easier?

Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018

Earlier work this paper cites.

The web as a knowledge-base for answering complex questions

Alon Talmor and Jonathan Berant. 2018 · 2018

Earlier work this paper cites.

Constructing datasets for multi-hop reading comprehension across documents

Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 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. 2018 · 2018

Earlier work this paper cites.

Understanding dataset design choices for multi-hop reasoning

Jifan Chen and Greg Durrett. 2019 · 2019

Earlier work this paper cites.

Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA

Yichen Jiang and Mohit Bansal. 2019 · 2019

Earlier work this paper cites.

Shortcut learning in deep neural networks

R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann. 2020 · 2020

Earlier work this paper cites.

Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps

Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020

Earlier work this paper cites.

R4C: A benchmark for evaluating RC systems to get the right answer for the right reason

Naoya Inoue, Pontus Stenetorp, and Kentaro Inui. 2020 · 2020

Earlier work this paper cites.

Is multihop QA in DiRe condition? measuring and reducing disconnected reasoning

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 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.

Do multi-hop question answering systems know how to answer the single-hop sub-questions?

Yixuan Tang, Hwee Tou Ng, and Anthony Tung. 2021 · 2021

Cited alongside, same era.

Successive prompting for decomposing complex questions

Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. 2022 · 2022

Cited alongside, same era.

Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

Cited alongside, same era.

Is a question decomposition unit all we need?

Pruthvi Patel, Swaroop Mishra, Mihir Parmar, and Chitta Baral. 2022 · 2022

Cited alongside, same era.

MuSiQue: Multihop questions via single-hop question composition

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022

Measuring and narrowing the compositionality gap in language models

Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah Smith, and Mike Lewis. 2023 · 2023

Later among the works it cites.

Reasoning with language model prompting: A survey

Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen. 2023 · 2023

Later among the works it cites.

When do decompositions help for machine reading?

Kangda Wei, Dawn Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller. 2023 · 2023

Later among the works it cites.

MQuAKE: Assessing knowledge editing in language models via multi-hop questions

Zexuan Zhong, Zhengxuan Wu, Christopher Manning, Christopher Potts, and Danqi Chen. 2023 · 2023

Later among the works it cites.

Least-to-most prompting enables complex reasoning in large language models

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023

Later among the works it cites.