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Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems.
REALM: Retrieval-Augmented Language Model Pre-Training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M. 2020 · 2002
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Dense Passage Retrieval for Open-Domain Question Answering
Karpukhin, V.; Oğuz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and tau Yih, W. 2020 · 2004
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Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K.; Evans, C.; Paritosh, P.; Sturge, T.; and Taylor, J. 2008 · 2008
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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 · 2009
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Wikidata: A Free Collaborative Knowledgebase
Vrandečić, D.; and Krötzsch, M. 2014 · 2014
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The Value of Semantic Parse Labeling for Knowledge Base Question Answering
Yih, W.; Richardson, M.; Meek, C.; Chang, M.; and Suh, J. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Chen, D.; Fisch, A.; Weston, J.; and Bordes, A. 2017 · 2017
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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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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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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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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
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DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters
Rasley, J.; Rajbhandari, S.; Ruwase, O.; and He, Y. 2020 · 2020
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Unsupervised data augmentation for consistency training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, T.; and Le, Q. 2020 · 2020
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Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. D. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
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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
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Finetuned language models are zero-shot learners
Wei, J.; Bosma, M.; Zhao, V. Y.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2021 · 2021
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FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Dao, T.; Fu, D. Y.; Ermon, S.; Rudra, A.; and Ré, C. 2022 · 2022
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CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Le, H.; Wang, Y.; Gotmare, A. D.; Savarese, S.; and Hoi, S. C. H. 2022 · 2022
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Data augmentation: A comprehensive survey of modern approaches
Mumuni, A.; and Mumuni, F. 2022 · 2022
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UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Oguz, B.; Chen, X.; Karpukhin, V.; Peshterliev, S.; Okhonko, D.; Schlichtkrull, M.; Gupta, S.; Mehdad, Y.; and Yih, S. 2022 · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; Chi, E. H.; Le, Q. V.; and Zhou, D. 2022 · 2022
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Bai, J.; Bai, S.; Chu, Y.; Cui, Z.; Dang, K.; Deng, X.; Fan, Y.; Ge, W.; Han, Y.; Huang, F.; Hui, B.; Ji, L.; Li, M.; Lin, J.; Lin, R.; Liu, D.; Liu, G.; Lu, C.; Lu, K.; Ma, J.; Men, R.; Ren, X.; Ren, X.; Tan, C.; Tan, S.; Tu, J.; Wang, P.; Wang, S.; Wang, W.; Wu, S.; Xu, B.; Xu, J.; Yang, A.; Yang, H.; Yang, J.; Yang, S.; Yao, Y.; Yu, B.; Yuan, H.; Yuan, Z.; Zhang, J.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhou, C.; Zhou, J.; Zhou, X.; and Zhu, T. 2023 · 2023
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Towards Scalable Automated Alignment of LLMs: A Survey
Cao, B.; Lu, K.; Lu, X.; Chen, J.; Ren, M.; Xiang, H.; Liu, P.; Lu, Y.; He, B.; Han, X.; et al. 2024 · 2024
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Scaling instruction-finetuned language models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, Y.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2024 · 2024
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How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
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Alpacafarm: A simulation framework for methods that learn from human feedback
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HFT: Half Fine-Tuning for Large Language Models
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
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Introducing Meta Llama 3: The most capable openly available LLM to date
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Benchmarking Complex Instruction-Following with Multiple Constraints Composition
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