Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context

Language models are few-shot learners

Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901

Earlier work this paper cites.

Generating followup questions for interpretable multi-hop question answering

Original

Christopher Malon and Bing Bai. 2020 · 2002

Earlier work this paper cites.

Large language models sensitivity to the order of options in multiple-choice questions

Pouya Pezeshkpour and Estevam Hruschka. 2024 · 2017

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 · 2018

Earlier work this paper cites.

Multi-hop reading comprehension through question decomposition and rescoring

Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019

Earlier work this paper cites.

Quick and (not so) dirty: Unsupervised selection of justification sentences for multi-hop question answering

Vikas Yadav, Steven Bethard, and Mihai Surdeanu. 2019 · 2019

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.

Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020

Earlier work this paper cites.

Unsupervised dense information retrieval with contrastive learning

Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021

Earlier work this paper cites.

Decomposed prompting: A modular approach for solving complex tasks

Original

Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2022 · 2022

Earlier work this paper cites.

Large language models are zero-shot reasoners

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

Earlier work this paper cites.

Internet-augmented language models through few-shot prompting for open-domain question answering

Original

Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. 2022 · 2022

Earlier work this paper cites.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022

Earlier work this paper cites.

MuSiQue: Multihop questions via single-hop question composition

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

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Earlier work this paper cites.

Retrieval-augmented generation for large language models: A survey

Original

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023

Cited alongside, same era.

Atlas: Few-shot learning with retrieval augmented language models

Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023 · 2023

Cited alongside, same era.

Active retrieval augmented generation

Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023b · 2023

Cited alongside, same era.

Lost in the middle: How language models use long contexts

Original

Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023

Cited alongside, same era.

Measuring and narrowing the compositionality gap in language models

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

The llama 3 herd of models

Original

Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024

Closest in time.

Sure: Summarizing retrievals using answer candidates for open-domain qa of llms

Jaehyung Kim, Jaehyun Nam, Sangwoo Mo, Jongjin Park, Sang-Woo Lee, Minjoon Seo, Jung-Woo Ha, and Jinwoo Shin. 2024 · 2024

Closest in time.

Can long-context language models subsume retrieval, rag, sql, and more?

Original

Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien MR Arnold, Vincent Perot, Siddharth Dalmia, et al. 2024 · 2024

Closest in time.

Making long-context language models better multi-hop reasoners

Yanyang Li, Shuo Liang, Michael Lyu, and Liwei Wang. 2024 · 2024

Closest in time.

Multi-hop question answering

Vaibhav Mavi, Anubhav Jangra, Adam Jatowt, et al. 2024 · 2024

Closest in time.