Fetching the paper…
Reading the bibliography…
Supply chain operations traditionally involve a variety of complex decision making problems.
Issues in supply chain management
Douglas M Lambert and Martha C Cooper · 2000
Earlier work this paper cites.
The gurobi optimizer
Bob Bixby · 2007
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Supply chain design and optimization: Challenges and opportunities
Daniel J Garcia and Fengqi You · 2015
Earlier work this paper cites.
The new generation of operations research methods in supply chain optimization: A review
Pourya Pourhejazy and Oh Kyoung Kwon · 2016
Earlier work this paper cites.
A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
Earlier work this paper cites.
Essentials of supply chain management
Michael H Hugos · 2018
Earlier work this paper cites.
Deep contextualized word representations, 2018
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Argumentation for explainable scheduling
Kristijonas Čyras, Dimitrios Letsios, Ruth Misener, and Francesca Toni · 2019
Earlier work this paper cites.
Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang · 2019
Earlier work this paper cites.
A survey of the state of explainable ai for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen · 2020
Earlier work this paper cites.
Turing-nlg: A 17-billion-parameter language model by microsoft
Corby Rosset · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Reframing human-ai collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi · 2021
Earlier work this paper cites.
From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where
Imran Ahmed, Gwanggil Jeon, and Francesco Piccialli · 2022
Cited alongside, same era.
Decision Optimization with IBM ILOG CPLEX Optimization Studio: A Hands-On Introduction to Modeling with the Optimization Programming Language (OPL)
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, and Wolfgang Burkart · 2022
Cited alongside, same era.
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways, 2022
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Closest in time.
Codecompose: A large-scale industrial deployment of ai-assisted code authoring
Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, and Nachiappan Nagappan · 2023
Closest in time.
Llm+ p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone · 2023
Closest in time.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
Closest in time.
ChatGPT plugins, 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis · 2022
Cited alongside, same era.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
Cited alongside, same era.
Contrastive learning reduces hallucination in conversations
Weiwei Sun, Zhengliang Shi, Shen Gao, Pengjie Ren, Maarten de Rijke, and Zhaochun Ren · 2022
Cited alongside, same era.
Compilable neural code generation with compiler feedback
Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, and Qun Liu · 2022
Cited alongside, same era.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine
Peter Lee, Sebastien Bubeck, and Joseph Petro · 2023
Cited alongside, same era.
OpenAI · 2023
Closest in time.
Function calling and other API updates, 2023
OpenAI · 2023
Closest in time.
Introduction — langchain, 2023
LangChian · 2023
Closest in time.
original-date: 2023-03-16T09:21:07Z
Auto-GPT: An Autonomous GPT-4 Experiment, June 2023 · 2023
Closest in time.
Translations: — BabyAGI, 2023
BabyAGI · 2023
Closest in time.
Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman · 2023
Closest in time.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales · 2023
Closest in time.
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al · 2023
Closest in time.
Skcoder: A sketch-based approach for automatic code generation
Jia Li, Yongmin Li, Ge Li, Zhi Jin, Yiyang Hao, and Xing Hu · 2023
Closest in time.
Self-planning code generation with large language model
Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, and Ge Li · 2023
Closest in time.
Improving code generation by training with natural language feedback, 2023
Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, and Ethan Perez · 2023
Closest in time.
Large language models as tool makers
Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou · 2023
Closest in time.
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
Closest in time.