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Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources.
Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk · 2007
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2009
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Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave · 2012
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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WikiQA: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek · 2015
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Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al · 2016
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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T-REx: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl · 2018
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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
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
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Skeleton-to-response: Dialogue generation guided by retrieval memory
Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, and Shuming Shi · 2019
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur 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 Le, and Slav Petrov · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
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Explanations for CommonsenseQA: New Dataset and Models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
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Efficient nearest neighbor language models
Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick · 2021
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Entity-based knowledge conflicts in question answering
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh · 2021
Cited alongside, same era.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
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Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
Cited alongside, same era.
End-to-end training of neural retrievers for open-domain question answering
Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, and Bryan Catanzaro · 2021
Cited alongside, same era.
Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston · 2021
Cited alongside, same era.
End-to-end training of multi-document reader and retriever for open-domain question answering
Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, and Dani Yogatama · 2021
Cited alongside, same era.
Hung-Ting Chen, Michael Zhang, and Eunsol Choi · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
Cited alongside, same era.
A survey on retrieval-augmented text generation
Huayang Li, Yixuan Su, Deng Cai, Yan Wang, and Lemao Liu · 2022
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei · 2022
Cited alongside, same era.
Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig · 2023
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C-pack: Packaged resources to advance general chinese embedding, 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
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Recomp: Improving retrieval-augmented lms with compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi · 2023
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Reliable, adaptable, and attributable language models with retrieval
Akari Asai, Zexuan Zhong, Danqi Chen, Pang Wei Koh, Luke Zettlemoyer, Hannaneh Hajishirzi, and Wen-tau Yih · 2024
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
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Ragged edges: The double-edged sword of retrieval-augmented chatbots
Philip Feldman Foulds, R James, and Shimei Pan · 2024
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Modular rag: Transforming rag systems into lego-like reconfigurable frameworks
Yunfan Gao, Yun Xiong, Meng Wang, and Haofen Wang · 2024
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Minicpm: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, et al · 2024
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Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models
Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, and Jun Zhao · 2024
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Step-dpo: Step-wise preference optimization for long-chain reasoning of llms
Xin Lai, Zhuotao Tian, Yukang Chen, Senqiao Yang, Xiangru Peng, and Jiaya Jia · 2024
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Agent q: Advanced reasoning and learning for autonomous ai agents
Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Instructrag: Instructing retrieval-augmented generation with explicit denoising
Zhepei Wei, Wei-Lin Chen, and Yu Meng · 2024
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Clasheval: Quantifying the tug-of-war between an llm’s internal prior and external evidence
Kevin Wu, Eric Wu, and James Zou · 2024
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Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su · 2024
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Corrective retrieval augmented generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling · 2024
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An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
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