Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks but face significant challenges with complex, knowledge-intensive multi-hop queries, particularly those involving new or long-tail knowledge.
Learning to recover reasoning chains for multi-hop question answering via cooperative games
Yufei Feng, Mo Yu, Wenhan Xiong, Xiaoxiao Guo, Junjie Huang, Shiyu Chang, Murray Campbell, Michael Greenspan, and Xiaodan Zhu. 2020 · 2004
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2005
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Stephen E. Robertson and Hugo Zaragoza. 2009 · 2009
Earlier work this paper cites.
Answering complex open-domain questions with multi-hop dense retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen tau Yih, Sebastian Riedel, Douwe Kiela, and Barlas Oğuz. 2020 · 2009
Earlier work this paper cites.
Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps
Xanh Ho, A. Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2011
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.
On the possibilities and limitations of multi-hop reasoning under linguistic imperfections
Daniel Khashabi, Erfan Sadeqi Azer, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2019 · 2019
Earlier work this paper cites.
Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc V. Le, and Slav Petrov. 2019 · 2019
Earlier work this paper cites.
Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 2019
Earlier work this paper cites.
A survey of knowledge-enhanced text generation
W. Yu, Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and Meng Jiang. 2020 · 2020
Earlier work this paper cites.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, and Yinfei Yang. 2021 · 2021
Earlier work this paper cites.
Musique: Multihop questions via single-hop question composition
H. Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2021 · 2021
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 · 2022
Earlier work this paper cites.
Large language models with controllable working memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, and Surinder Kumar. 2022 · 2022
Earlier work this paper cites.
When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Troy Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi. 2022 · 2022
Earlier work this paper cites.
Mintaka: A complex, natural, and multilingual dataset for end-to-end question answering
Priyanka Sen, Alham Fikri Aji, and Amir Saffari. 2022 · 2022
Earlier work this paper cites.
One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
Earlier work this paper 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
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
Evaluating open-domain question answering in the era of large language models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, and . Christian Keller etc. 2024 · 2024
Closest in time.
Qwen2.5-coder technical report
Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, Kai Dang, Yang Fan, Yichang Zhang, An Yang, Rui Men, Fei Huang, Bo Zheng, Yibo Miao, Shanghaoran Quan, Yunlong Feng, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, and Junyang Lin. 2024 · 2024
Closest in time.
Open-rag: Enhanced retrieval-augmented reasoning with open-source large language models
Shayekh Bin Islam, Md Asib Rahman, K S M Tozammel Hossain, Enamul Hoque, Shafiq R. Joty, and Md. Rizwan Parvez. 2024 · 2024
Closest in time.
Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ehsan Kamalloo, Nouha Dziri, Charles L. A. Clarke, and Davood Rafiei. 2023 · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Haotong Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Self-adaptive reasoning on sub-questions for multi-hop question answering
Zekai Li and Wei Peng. 2023 · 2023
Cited alongside, same era.
Investigating the factual knowledge boundary of large language models with retrieval augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, J. Liu, Hao Tian, Huaqin Wu, Ji rong Wen, and Haifeng Wang. 2023 · 2023
Cited alongside, same era.
Kai Sun, Y. Xu, Hanwen Zha, Yue Liu, and Xinhsuai Dong. 2023 · 2023
Cited alongside, same era.
Freshllms: Refreshing large language models with search engine augmentation
Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2023 · 2023
Cited alongside, same era.
Self-prompted chain-of-thought on large language models for open-domain multi-hop reasoning
Jinyuan Wang, Junlong Li, and Hai Zhao. 2023 · 2023
Cited alongside, same era.
Lm-cocktail: Resilient tuning of language models via model merging
Shitao Xiao, Zheng Liu, Peitian Zhang, and Xingrun Xing. 2023 · 2023
Cited alongside, same era.
Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C. Park. 2024 · 2024
Closest in time.
Ra-isf: Learning to answer and understand from retrieval augmentation via iterative self-feedback
Yanming Liu, Xinyue Peng, Xuhong Zhang, Weihao Liu, Jianwei Yin, Jiannan Cao, and Tianyu Du. 2024 · 2024
Closest in time.
Seiji Maekawa, Hayate Iso, Sairam Gurajada, and Nikita Bhutani. 2024 · 2024
Closest in time.
When do llms need retrieval augmentation? mitigating llms’ overconfidence helps retrieval augmentation
Shiyu Ni, Keping Bi, J. Guo, and Xueqi Cheng. 2024 · 2024
Closest in time.
Fine tuning vs. retrieval augmented generation for less popular knowledge
Heydar Soudani, Evangelos Kanoulas, and Faegheh Hasibi. 2024 · 2024
Closest in time.
Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries
Yixuan Tang and Yi Yang. 2024 · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, and Sammy Jerome etc. 2024 · 2024
Closest in time.
FreshLLMs: Refreshing large language models with search engine augmentation
Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2024 · 2024
Closest in time.
No need for large-scale search: Exploring large language models in complex knowledge base question answering
Shouhui Wang and Biao Qin. 2024 · 2024
Closest in time.
Searching for best practices in retrieval-augmented generation
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, et al. 2024 · 2024
Closest in time.
Promptriever: Instruction-trained retrievers can be prompted like language models
Orion Weller, Benjamin Van Durme, Dawn Lawrie, Ashwin Paranjape, Yuhao Zhang, and Jack Hessel. 2024 · 2024
Closest in time.
Zihan Zhang, Meng Fang, and Ling Chen. 2024 · 2024
Closest in time.
Efficientrag: Efficient retriever for multi-hop question answering
Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, S. Rajmohan, Dongmei Zhang, and Qi Zhang. 2024 · 2024
Closest in time.
Deliberate reasoning in language models as structure-aware planning with an accurate world model
Siheng Xiong, Ali Payani, Yuan Yang, and Faramarz Fekri. 2025 · 2025
Closest in time.