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Augmenting LLMs with context leads to improved performance across many applications.
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
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
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
Earlier work this paper cites.
Retrieving and reading: A comprehensive survey on open-domain question answering
Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua · 2021
Earlier work this paper cites.
True: Re-evaluating factual consistency evaluation
Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias · 2022
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
Earlier work this paper cites.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al · 2022
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On the epistemic limits of personalized prediction
Lucas Monteiro Paes, Carol Xuan Long, Berk Ustun, and Flavio Calmon · 2022
Earlier work this paper cites.
Musique: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Earlier work this paper cites.
AQ Jiang, A Sablayrolles, A Mensch, C Bamford, DS Chaplot, D de las Casas, F Bressand, G Lengyel, G Lample, L Saulnier, et al · 2023
Earlier work this paper cites.
Participatory personalization in classification
Hailey Joren, Chirag Nagpal, Katherine A Heller, and Berk Ustun · 2023
Earlier work this paper cites.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
Earlier work this paper cites.
Measuring attribution in natural language generation models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Lora Aroyo, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter · 2023
Earlier work this paper cites.
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, et al · 2023
Cited alongside, same era.
Do large language models know what they don’t know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang · 2023
Cited alongside, same era.
Claude 3.5 sonnet model card addendum, 2024
Anthropic · 2024
Cited alongside, same era.
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
Cited alongside, same era.
Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, Ranjay Krishna, Yoon Kim, and James Glass · 2024
Online joint fine-tuning of multi-agent flows
Paul Mineiro · 2024
Closest in time.
Fine-grained hallucination detection and editing for language models
Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov, and Hannaneh Hajishirzi · 2024
Closest in time.
RAGTruth: A hallucination corpus for developing trustworthy retrieval-augmented language models
Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, and Tong Zhang · 2024
Closest in time.
Bergen: A benchmarking library for retrieval-augmented generation
David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Vassilina Nikoulina, and Stéphane Clinchant · 2024
Closest in time.
Ragchecker: A fine-grained framework for diagnosing retrieval-augmented generation
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Cited alongside, same era.
The power of noise: Redefining retrieval for rag systems
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri · 2024
Cited alongside, same era.
A survey on rag meeting llms: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
Cited alongside, same era.
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, et al · 2024
Cited alongside, same era.
Found in the middle: Calibrating positional attention bias improves long context utilization
Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister · 2024
Cited alongside, same era.
Retrieve, summarize, plan: Advancing multi-hop question answering with an iterative approach
Zhouyu Jiang, Mengshu Sun, Lei Liang, and Zhiqiang Zhang · 2024
Cited alongside, same era.
Flashrag: A modular toolkit for efficient retrieval-augmented generation research
Jiajie Jin, Yutao Zhu, Xinyu Yang, Chenghao Zhang, and Zhicheng Dou · 2024
Cited alongside, same era.
Classification with conceptual safeguards
Hailey Joren, Charles Marx, and Berk Ustun · 2024
Cited alongside, same era.
Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Jiayang Cheng, Cunxiang Wang, Shichao Sun, Huanyu Li, et al · 2024
Closest in time.
Constructing benchmarks and interventions for combating hallucinations in llms
Adi Simhi, Jonathan Herzig, Idan Szpektor, and Yonatan Belinkov · 2024
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Foundational autoraters: Taming large language models for better automatic evaluation
Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, and Yun-Hsuan Sung · 2024
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How easily do irrelevant inputs skew the responses of large language models?
Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, and Yanghua Xiao · 2024
Closest in time.
Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su · 2024
Closest in time.
Corrective retrieval augmented generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling · 2024
Closest in time.
Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant · 2024
Closest in time.
In defense of rag in the era of long-context language models
Tan Yu, Anbang Xu, and Rama Akkiraju · 2024
Closest in time.
R-tuning: Instructing large language models to say ‘i don’t know’
Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang · 2024
Closest in time.
End-to-end beam retrieval for multi-hop question answering
Jiahao Zhang, Haiyang Zhang, Dongmei Zhang, Liu Yong, and Shen Huang · 2024
Closest in time.
Metacognitive retrieval-augmented large language models
Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, and Zhicheng Dou · 2024
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