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With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Adaptive Mixtures of Local Experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton. 1991 · 1991
Earlier work this paper cites.
Scaling Large Learning Problems with Hard Parallel Mixtures
Ronan Collobert, Yoshua Bengio, and Samy Bengio. 2002 · 2002
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M Bishop. 2006 · 2006
Earlier work this paper cites.
Learning Factored Representations in a Deep Mixture of Experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever. 2013 · 2013
Earlier work this paper cites.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer. 2017 · 2017
Earlier work this paper cites.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
Sebastian Raschka. 2020 · 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 · 2021
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Measuring Massive Multitask Language Understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Earlier work this paper cites.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
Yixin Liu and Pengfei Liu. 2021 · 2021
Earlier work this paper cites.
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Mathieu Ravaut, Shafiq Joty, and Nancy Chen. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Large Language Model Routing with Benchmark Datasets
Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, and Mikhail Yurochkin. 2023 · 2023
Later among the works it cites.
Evaluating the Factual Consistency of Large Language Models Through News Summarization
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel. 2023 · 2023
Later among the works it cites.
Fusing Models with Complementary Expertise
Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric Xing, and Mikhail Yurochkin. 2023 · 2023
Later among the works it cites.
Chain of Thought Prompting Elicits Knowledge Augmentation
Dingjun Wu, Jing Zhang, and Xinmei Huang. 2023a · 2023
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Sentiment Analysis in the Era of Large Language Models: A Reality Check
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Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, Karen Khatamifard, Minsik Cho, Carlo C Del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar. 2023 · 2023
Cited alongside, same era.
Open LLM Leaderboard
Edward Beeching, Clémentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, and Thomas Wolf. 2023 · 2023
Cited alongside, same era.
A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2023 · 2023
Cited alongside, same era.
Fast inference of mixture-of-experts language models with offloading
Artyom Eliseev and Denis Mazur. 2023 · 2023
Cited alongside, same era.
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. 2023 · 2023
Cited alongside, same era.
Camel: Communicative agents for "mind" exploration of large scale language model society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023 · 2023
Cited alongside, same era.
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
Ella Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi, and Ateret Anaby-Tavor. 2023 · 2023
Cited alongside, same era.
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. 2023 · 2023
Later among the works it cites.
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Rühle, Laks V. S. Lakshmanan, and Ahmed Hassan Awadallah. 2024 · 2024
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Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2024 · 2024
Closest in time.
Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. 2024 · 2024
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Large language models: A survey
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. 2024 · 2024
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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024 · 2024
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What makes math word problems challenging for llms?
KV Aditya Srivatsa and Ekaterina Kochmar. 2024 · 2024
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Are NLP Models really able to Solve Simple Math Word Problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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