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The rapidly growing number and variety of Large Language Models (LLMs) present significant challenges in efficiently selecting the appropriate LLM for a given query, especially considering the trade-offs between performance and computational cost.
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu · 2019
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Fine-grained event categorization with heterogeneous graph convolutional networks
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Heterogeneous graph attention network
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Junyu Gao and Changsheng Xu · 2020
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2020
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Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
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Graph convolutional networks with markov random field reasoning for social spammer detection
Yongji Wu, Defu Lian, Yiheng Xu, Le Wu, and Enhong Chen · 2020
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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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Liangwei Yang, Zhiwei Liu, Yingtong Dou, Jing Ma, and Philip S Yu · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Shima Imani, Liang Du, and Harsh Shrivastava · 2023
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Tcra-llm: Token compression retrieval augmented large language model for inference cost reduction
Junyi Liu, Liangzhi Li, Tong Xiang, Bowen Wang, and Yiming Qian · 2023
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Stanford alpaca: An instruction-following llama model, 2023
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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On optimal caching and model multiplexing for large model inference
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Gndan: Graph navigated dual attention network for zero-shot learning
Shiming Chen, Ziming Hong, Guosen Xie, Qinmu Peng, Xinge You, Weiping Ding, and Ling Shao · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al · 2022
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Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark Barrett, Michael I Jordan, and Jiantao Jiao · 2023
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Studying llm performance on closed-and open-source data
Toufique Ahmed, Christian Bird, Premkumar Devanbu, and Saikat Chakraborty · 2024
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