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Fusing knowledge from multiple Large Language Models (LLMs) can combine their diverse strengths to achieve improved performance on a given task.
Random decision forests
Tin Kam Ho · 1995
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Learning with ensembles: How overfitting can be useful
Peter Sollich and Anders Krogh · 1995
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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila I Kuncheva and Christopher J Whitaker · 2003
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The boosting approach to machine learning: An overview
Robert E Schapire · 2003
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On the relationship between classical grid search and probabilistic roadmaps
Steven M LaValle, Michael S Branicky, and Stephen R Lindemann · 2004
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Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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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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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti · 2019
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu · 2019
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S2orc: The semantic scholar open research corpus
Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Dan S Weld · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Noam Shazeer, Peter J Liu, Sharan Narang, Wei Li, and Yanqi Zhou · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
Llm-blender: Ensembling large language models with pairwise comparison and generative fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin · 2023
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Textbooks are all you need ii: phi-1.5
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee · 2023
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When less is more: Investigating data pruning for pretraining llms at scale
Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, and Sara Hooker · 2023
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Which examples to annotate for in-context learning? towards effective and efficient selection
Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, and George Karypis · 2023
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What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits · 2021
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi · 2021
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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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Sparse upcycling: Training mixture-of-experts from dense checkpoints
Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Adapting large language models via reading comprehension
Daixuan Cheng, Shaohan Huang, and Furu Wei · 2023
Cited alongside, same era.
Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot · 2023
Cited alongside, same era.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Fusing models with complementary expertise
Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric Xing, and Mikhail Yurochkin · 2023
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Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
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Mastering text, code and math simultaneously via fusing highly specialized language models
Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, and Maosong Sun · 2024
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Knowledge card: Filling llms’ knowledge gaps with plug-in specialized language models
Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, and Yulia Tsvetkov · 2024
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Biomistral: A collection of open-source pretrained large language models for medical domains
Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour · 2024
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Tuning language models by proxy
Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A Smith · 2024
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Branch-train-mix: Mixing expert llms into a mixture-of-experts llm
Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma, Hu Xu, Xi Victoria Lin, Baptiste Rozière, Jacob Kahn, Daniel Li, Wen-tau Yih, Jason Weston, et al · 2024
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Knowledge fusion of large language models
Fanqi Wan, Xinting Huang, Deng Cai, Xiaojun Quan, Wei Bi, and Shuming Shi · 2024
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