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Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) for generating more factual, accurate, and up-to-date content.
UnifiedQA: Crossing Format Boundaries With a Single QA System
Khashabi, D.; Min, S.; Khot, T.; Sabharwal, A.; Tafjord, O.; Clark, P.; and Hajishirzi, H. 2020 · 1907
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The Probabilistic Relevance Framework: BM25 and Beyond
Robertson, S. E.; and Zaragoza, H. 2009 · 2009
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Semantic Parsing on Freebase from Question-Answer Pairs
Berant, J.; Chou, A.; Frostig, R.; and Liang, P. 2013 · 2013
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Nguyen, T.; Rosenberg, M.; Song, X.; Gao, J.; Tiwary, S.; Majumder, R.; and Deng, L. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100, 000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M.; Choi, E.; Weld, D. S.; and Zettlemoyer, L. 2017 · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.; Bilen, H.; and Vedaldi, A. 2017 · 2017
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W. W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
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BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Clark, C.; Lee, K.; Chang, M.; Kwiatkowski, T.; Collins, M.; and Toutanova, K. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019 · 2019
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Parameter-Efficient Transfer Learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; de Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
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How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI
Kalyan, A.; Kumar, A.; Chandrasekaran, A.; Sabharwal, A.; and Clark, P. 2021 · 2019
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Natural Questions: a Benchmark for Question Answering Research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A. P.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; Toutanova, K.; Jones, L.; Kelcey, M.; Chang, M.; Dai, A. M.; Uszkoreit, J.; Le, Q.; and Petrov, S. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E. Z.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
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CoQA: A Conversational Question Answering Challenge
Reddy, S.; Chen, D.; and Manning, C. D. 2019 · 2019
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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Talmor, A.; Herzig, J.; Lourie, N.; and Berant, J. 2019 · 2019
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
Earlier work this paper cites.
A Simple Framework for Contrastive Learning of Visual Representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. E. 2020 · 2020
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Retrieval Augmented Language Model Pre-Training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M. 2020 · 2020
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Momentum Contrast for Unsupervised Visual Representation Learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. B. 2020 · 2020
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X.; Nguyen, A. D.; Sugawara, S.; and Aizawa, A. 2020 · 2020
Earlier work this paper cites.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P. S. H.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.; Rocktäschel, T.; Riedel, S.; and Kiela, D. 2020 · 2020
Cited alongside, same era.
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Lin, Z.; Madotto, A.; and Fung, P. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Cited alongside, same era.
Training Verifiers to Solve Math Word Problems
Cobbe, K.; Kosaraju, V.; Bavarian, M.; Chen, M.; Jun, H.; Kaiser, L.; Plappert, M.; Tworek, J.; Hilton, J.; Nakano, R.; Hesse, C.; and Schulman, J. 2021 · 2021
Cited alongside, same era.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
Cited alongside, same era.
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Mallen, A.; Asai, A.; Zhong, V.; Das, R.; Khashabi, D.; and Hajishirzi, H. 2023 · 2023
Later among the works it cites.
GPT-4 Technical Report
OpenAI. 2023 · 2023
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Measuring and Narrowing the Compositionality Gap in Language Models
Press, O.; Zhang, M.; Min, S.; Schmidt, L.; Smith, N. A.; and Lewis, M. 2023 · 2023
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In-Context Retrieval-Augmented Language Models
Ram, O.; Levine, Y.; Dalmedigos, I.; Muhlgay, D.; Shashua, A.; Leyton-Brown, K.; and Shoham, Y. 2023 · 2023
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REPLUG: Retrieval-Augmented Black-Box Language Models
Shi, W.; Min, S.; Yasunaga, M.; Seo, M.; James, R.; Lewis, M.; Zettlemoyer, L.; and Yih, W. 2023 · 2023
Later among the works it cites.
LLaMA: Open and Efficient Foundation Language Models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; Rodriguez, A.; Joulin, A.; Grave, E.; and Lample, G. 2023 · 2023
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Measuring Massive Multitask Language Understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2021 · 2021
Cited alongside, same era.
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
KILT: a Benchmark for Knowledge Intensive Language Tasks
Petroni, F.; Piktus, A.; Fan, A.; Lewis, P. S. H.; Yazdani, M.; Cao, N. D.; Thorne, J.; Jernite, Y.; Karpukhin, V.; Maillard, J.; Plachouras, V.; Rocktäschel, T.; and Riedel, S. 2021 · 2021
Cited alongside, same era.
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Qin, G.; and Eisner, J. 2021 · 2021
Cited alongside, same era.
Improving Language Models by Retrieving from Trillions of Tokens
Borgeaud, S.; Mensch, A.; Hoffmann, J.; Cai, T.; Rutherford, E.; Millican, K.; van den Driessche, G.; Lespiau, J.; Damoc, B.; Clark, A.; de Las Casas, D.; Guy, A.; Menick, J.; Ring, R.; Hennigan, T.; Huang, S.; Maggiore, L.; Jones, C.; Cassirer, A.; Brock, A.; Paganini, M.; Irving, G.; Vinyals, O.; Osindero, S.; Simonyan, K.; Rae, J. W.; Elsen, E.; and Sifre, L. 2022 · 2022
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2023 · 2023
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Efficient Large Language Models: A Survey
Wan, Z.; Wang, X.; Liu, C.; Alam, S.; Zheng, Y.; Liu, J.; Qu, Z.; Yan, S.; Zhu, Y.; Zhang, Q.; Chowdhury, M.; and Zhang, M. 2023 · 2023
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C-Pack: Packaged Resources To Advance General Chinese Embedding
Xiao, S.; Liu, Z.; Zhang, P.; and Muennighof, N. 2023 · 2023
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Making Retrieval-Augmented Language Models Robust to Irrelevant Context
Yoran, O.; Wolfson, T.; Ram, O.; and Berant, J. 2023 · 2023
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Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
Yu, Z.; Xiong, C.; Yu, S.; and Liu, Z. 2023 · 2023
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Large Language Models for Information Retrieval: A Survey
Zhu, Y.; Yuan, H.; Wang, S.; Liu, J.; Liu, W.; Deng, C.; Dou, Z.; and Wen, J. 2023 · 2023
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2024 · 2024
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TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation
Fang, J.; Meng, Z.; and Macdonald, C. 2024 · 2024
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Retrieval-Generation Synergy Augmented Large Language Models
Feng, Z.; Feng, X.; Zhao, D.; Yang, M.; and Qin, B. 2024 · 2024
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CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P.; Geng, S.; Zhang, R.; Ma, T.; Fang, R.; Zhang, Y.; Li, H.; and Qiao, Y. 2024 · 2024
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SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Kim, J.; Nam, J.; Mo, S.; Park, J.; Lee, S.; Seo, M.; Ha, J.; and Shin, J. 2024 · 2024
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Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
Tan, J.; Dou, Z.; Zhu, Y.; Guo, P.; Fang, K.; and Wen, J. 2024 · 2024
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How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
Wu, S.; Xie, J.; Chen, J.; Zhu, T.; Zhang, K.; and Xiao, Y. 2024 · 2024
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RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation
Xu, F.; Shi, W.; and Choi, E. 2024 · 2024
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INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Zhu, Y.; Zhang, P.; Zhang, C.; Chen, Y.; Xie, B.; Dou, Z.; Liu, Z.; and Wen, J. 2024 · 2024
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MTEB: Massive Text Embedding Benchmark
Muennighoff, N.; Tazi, N.; Magne, L.; and Reimers, N. 2023 · 2029
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