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Large language models (LLMs) are increasingly used in applications where LLM inputs may span many different tasks.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
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Hierarchical mixtures of experts and the em algorithm
M.I. Jordan and R.A. Jacobs · 1993
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Sentence fusion for multidocument news summarization
Regina Barzilay and Kathleen R. McKeown · 2005
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Squad: 100,000+ questions for machine comprehension of text, 2016
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Creating training corpora for NLG micro-planners
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini · 2017
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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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The e2e dataset: New challenges for end-to-end generation, 2017
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser · 2017
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Snorkel: rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
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Data programming: Creating large training sets, quickly, 2017
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, 2017
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Frustratingly easy model ensemble for abstractive summarization
Hayato Kobayashi · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization, 2018
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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Training complex models with multi-task weak supervision, 2018
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2018
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Handling rare items in data-to-text generation
Anastasia Shimorina and Claire Gardent · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Learning dependency structures for weak supervision models, 2019
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, and Christopher Ré · 2019
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Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Re · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2020
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A cascade approach to neural abstractive summarization with content selection and fusion, 2020
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Walter Chang, and Fei Liu · 2020
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Learning to fuse sentences with transformers for summarization, 2020
Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, and Fei Liu · 2020
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Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 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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Reordering examples helps during priming-based few-shot learning
Sawan Kumar and Partha Talukdar · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2021
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Ask me anything: A simple strategy for prompting language models, 2022
Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré · 2022
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An error consistency based approach to answer aggregation in open-ended crowdsourcing
Lei Chai, Hailong Sun, and Zizhe Wang · 2022
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Data curation alone can stabilize in-context learning
Ting-Yun Chang and Robin Jia · 2022
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Shoring up the foundations: fusing model embeddings and weak supervision
Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2022
William Fedus, Barret Zoph, and Noam Shazeer · 2022
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer · 2022
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Chonghua Liao, Yanan Zheng, and Zhilin Yang · 2022
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Boosted prompt ensembles for large language models
Silviu Pitis, Michael R Zhang, Andrew Wang, and Jimmy Ba · 2023
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Summareranker: A multi-task mixture-of-experts re-ranking framework for abstractive summarization, 2023
Mathieu Ravaut, Shafiq Joty, and Nancy F. Chen · 2023
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Towards summary candidates fusion, 2023
Mathieu Ravaut, Shafiq Joty, and Nancy F. Chen · 2023
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Large language model routing with benchmark datasets, 2023
Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, and Mikhail Yurochkin · 2023
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Getting more out of mixture of language model reasoning experts, 2023
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber · 2023
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Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey, Chien-Sheng Wu, and Caiming Xiong · 2022
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Universalizing weak supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Carl Roberts, and Frederic Sala · 2022
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An information-theoretic approach to prompt engineering without ground truth labels
Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jeffrey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate · 2022
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Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al · 2022
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Lifting weak supervision to structured prediction, 2022
Harit Vishwakarma, Nicholas Roberts, and Frederic Sala · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong · 2022
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A bi-objective ϵ \epsilon -constrained framework for quality-cost optimization in language model ensembles, 2023
Aditi Singla, Aditya Singh, and Kanishk Kukreja · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 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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Improving probability-based prompt selection through unified evaluation and analysis
Sohee Yang, Jonghyeon Kim, Joel Jang, Seonghyeon Ye, Hyunji Lee, and Minjoon Seo · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Simple linear attention language models balance the recall-throughput tradeoff, 2024
Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, Dylan Zinsley, James Zou, Atri Rudra, and Christopher Ré · 2024
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Large language monkeys: Scaling inference compute with repeated sampling, 2024
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Are more llm calls all you need? towards scaling laws of compound inference systems, 2024
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Rephrase and respond: Let large language models ask better questions for themselves, 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
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Routerbench: A benchmark for multi-llm routing system, 2024
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Learning to route among specialized experts for zero-shot generalization, 2024
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OpenAI · 2024
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